drivers and effects of internationalization in the european

UNIVERSITEIT GENT

FACULTEIT ECONOMIE EN BEDRIJFSKUNDE

ACADEMIEJAAR 2015 – 2016

Drivers and effects of internationalization in the European construction industry

Masterproef voorgedragen tot het bekomen van de graad van

Master of Science in de Bedrijfseconomie

Bart Meersschaert

onder leiding van

Prof. Dr. Philippe Van Cauwenberge

I

REPRINTS AND PERMISSIONS

“The author gives permission to make this master dissertation available for consultation and to

copy parts of this master dissertation, provided that the source is quoted correctly.”

“Ondergetekende verklaart dat de inhoud van deze masterproef mag geraadpleegd en/of

gereproduceerd worden, mits bronvermelding.”

Gent, januari 2016

Bart Meersschaert

III

SAMENVATTING

Deze thesis focust op de motivatie tot internationalisering bij Europese bedrijven in de architectuur-,

ingenieurs- en bouwsector (AEC-sector) en op de effecten van internationalisering op hun

winstgevendheid en kapitaalstructuur. Deze studie is uitgevoerd door middel van empirisch

onderzoek en numerieke regressies met data gebaseerd op gegevens van dochterondernemingen

onder controle van de betreffende bedrijven.

Internationalisering in al haar vormen gaat steeds gepaard met verschillende risicofactoren.

Daarnaast wordt de AEC-sector eveneens gekenmerkt door een hoge financiële kwetsbaarheid. De

combinatie van het internationaliseringsrisico en het financiële risico duiden op het belang van

onderzoek rond dit onderwerp. In deze thesis wordt specifiek het effect van internationalisering op

de winstgevendheid en op de schuldgraad van bedrijven in deze sector onderzocht.

Ondanks de verschillende risico’s waar bouwkundige bedrijven mee geconfronteerd worden bij de

internationalisering van hun bedrijfsactiviteiten zijn er toch bedrijven die de sprong wagen. De

motiverende factoren achter deze beslissing worden daarom ook verder doorgrond.

Omwille van de beperkte beschikbaarheid van exportgegevens voor deze thesis, is een alternatieve

methode ontwikkeld op basis van beschikbare data van dochterondernemingen. Deze methode heeft

weliswaar haar beperkingen, maar laat ook toe om het onderwerp vanuit een andere invalshoek te

bekijken.

In hoofdstukken 2 en 3 wordt het theoretisch kader geschetst en worden hypotheses opgesteld.

Hoofdstuk 4 behandelt vervolgens de gebruikte werkmethodes, waarna de resultaten worden

besproken in hoofdstuk 5.

Via empirisch onderzoek wordt in deze thesis vastgesteld dat het merendeel van de dochterbedrijven

van Europese bedrijven in de architectuur-, ingenieurs- en bouwsector in hetzelfde land gelegen zijn

als het moederbedrijf. Daarnaast vormen ook de andere EU-lidstaten aantrekkelijke locaties om een

dochterbedrijf in op te richten of over te nemen.

De winstgevendheid fluctueert in functie van de graad van internationalisering in drie fases. In een

eerste fase heeft het bedrijf in kwestie nog weinig ervaring op het internationale speelveld en wegen

de opbrengsten nog niet op tegen de kosten van internationalisering. In een volgende fase heeft het

bedrijf reeds meer ervaring opgedaan en kan het deze ervaring omzetten in hogere winsten. In de

IV SAMENVATTING

derde fase stijgen de coördinatiekosten echter exponentieel waardoor de additionele winsten

verdampen.

Wat de kapitaalstructuur betreft, tonen de resultaten dat internationaal actievere bouwkundige

bedrijven minder gefinancierd worden met schulden (voornamelijk langetermijnschulden) en meer

met eigen vermogen.

Wat de motivaties voor internationale uitbreiding betreft, heeft dit onderzoek meerdere invloeden

aan het licht gebracht. Enerzijds heeft de grootte van het bedrijf een positieve invloed op het

internationaliseringsgedrag van ondernemingen in de Europese bouwsector. Ook zorgt de

combinatie van een hogere omzet in het buitenland en een lagere omzet in het binnenland voor een

positief effect. Het internationaliseringsproces verloopt in fases van verhoogde interesse en lagere

interesse. Daarnaast heeft ook de leeftijd van een bouwbedrijf een invloed op de spreiding van

dochterondernemingen. Naarmate bedrijven ouder worden, neemt het aandeel van hun Europese

dochterondernemingen af, terwijl het belang van dochterondernemingen buiten Europa toeneemt.

Tot slot is bij deze studie ook gebleken dat er naast bovenvermelde effecten nog ongespecificeerde

bedrijfsspecifieke effecten meespelen.

In hoofdstuk 6 wordt tot slot een afweging gemaakt van de resultaten en de beperkingen van de

gebruikte methode, waarna suggesties aangebracht worden voor verder onderzoek binnen dit

domein.

V

FOREWORD

The dissertation before you is the final piece of my education on the subject of business economics.

My motivation for this study on the topic of internationalization in the construction industry lies in

that it forms the intersection of a couple of my interests. On one hand it is an extension to my prior

education on civil engineering. On the other hand it encompasses my interests in business strategy

and globalization.

First of all I want to thank Prof. Dr. Philippe Van Cauwenberge for making this dissertation possible.

Even though the topic of this thesis is not at the core of his research field, he accepted to supervise

my study on the subject of business internationalization in the European construction sector. I thank

him for his wise advice and his devoted guidance throughout the writing process. I could not wish for

a better supervisor.

For their help with gathering the necessary data and executing the regressions, I thank Prof. Dr.

Bruno Merlevede and Peter Beyne. Without their help, I would not have been able to finish this

dissertation properly.

Furthermore I thank Prof. Dr. Philippe Van Cauwenberge, Peter Beyne, Sigrid Rumes, Stefaan De

Caluwé, Stefanie D’Hondt and Chloé Vanheuverswyn for their advice and their help in revising parts

of this text.

I would also like to thank my parents, my brother and my sister for supporting me in everything I do.

Without them I would not be who I am today and without their proper support, I might not have

been able to start the study subject that I am now finishing with this dissertation.

Finally, I want to thank all my friends and family for backing me up whenever I felt discouraged, for

taking my mind off this dissertation any time I needed distraction and for always being there for me.

VII

TABLE OF CONTENTS

REPRINTS AND PERMISSIONS ................................................................................................................ I

DUTCH SUMMARY ........................................................................................................................... III

FOREWORD ..................................................................................................................................... V

TABLE OF CONTENTS ........................................................................................................................ VII

LIST OF ABBREVIATIONS .................................................................................................................... IX

LIST OF TABLES .............................................................................................................................. XIII

LIST OF FIGURES ............................................................................................................................. XV

1. INTRODUCTION AND RESEARCH QUESTIONS ......................................................................................... 1

2. THEORETICAL BACKGROUND ............................................................................................................ 3

Evolution of the international economic environment ....................................................................... 3

The process of business internationalization ...................................................................................... 5

AEC industry ........................................................................................................................................ 8

3. LITERATURE REVIEW AND HYPOTHESES ............................................................................................. 11

Profitability ........................................................................................................................................ 11

Indebtedness ..................................................................................................................................... 14

Drivers of internationalization .......................................................................................................... 15

4. RESEARCH METHOD AND DATA ....................................................................................................... 19

Data ................................................................................................................................................... 19

Method .............................................................................................................................................. 19

Dependent variables ......................................................................................................................... 20

Independent variables and estimation models ................................................................................. 25

5. PRIMARY RESULTS ....................................................................................................................... 29

Empirical subsidiary study ................................................................................................................. 29

Comparison of export data and subsidiary data ............................................................................... 38

VIII TABLE OF CONTENTS

Profitability ........................................................................................................................................ 41

Indebtedness ..................................................................................................................................... 45

Drivers of internationalization .......................................................................................................... 49

6. CONCLUSION, DISCUSSION, AND LIMITATIONS .................................................................................... 53

REFERENCES .................................................................................................................................. 57

ANNEXES ........................................................................................................................... ANNEX 1.1

Annex 1: Size distribution of sample firms by industry ......................................................... Annex 1.1

Annex 2: Emerging economies .............................................................................................. Annex 2.1

Annex 3: Geographical spread of subsidiaries in the different UN subregions .................... Annex 3.1

Annex 4: Comparison of export data and subsidiary data – regression results .................... Annex 4.1

Annex 5: Profitability relation – descriptive statistics ........................................................... Annex 5.1

Annex 6: Profitability relation – regression results ............................................................... Annex 6.1

Annex 7: Indebtedness relations – descriptive statistics ...................................................... Annex 7.1

Annex 8: Indebtedness relation – regression results ............................................................ Annex 8.1

Annex 9: Drivers of internationalization – descriptive statistics ........................................... Annex 9.1

Annex 10: Drivers of internationalization – regression results ........................................... Annex 10.1

IX

LIST OF ABBREVIATIONS

AEC

EC

ECB

ECSC

EEA

EEC

EFTA

EU

EURATOM

FDI

GATT

IMF

ITO

MNE

NACE

UNCTAD

WTO

Architecture, Engineering & Contractor industry

European Community

European Central Bank

European Coal and Steel Community

European Economic Area

European Economic Community

European Free Trade Agreement

European Union

European Atomic Energy Community

Foreign Direct Investment

General Agreement on Tariffs and Trade

International Monetary Fund

International Trade Organisation

MultiNational Enterprise

“Nomenclature statistique des Activités économiques dans la

Communauté Européenne” (French for “Statistical Classification of

Economic Activities in the European Community”)

United Nations Conference on Trade And Development

World Trade Organisation

X LIST OF ABBREVIATIONS

Regression variables

[...]2

Δ[...]

Δ[...]2

Definition adapted to European subsidiaries

Definition adapted to all subsidiaries by extrapolating the values of

the European subsidiaries

Two-year average value

Percentage growth

Percentage growth over the last two years

AGE

CRIS

DDS

EBIT

FATA

FETE

FSTS

GDP

IED

IFS

IND

INT

ITA

LDA

LOC

Age of the company

Crisis variable measuring GDP growth

Dummy variable for the Decrease in Domestic Sales

Earnings Before Interests and Taxes

Foreign Assets over Total Assets

Foreign Employees over Total Employees

Foreign Sales over Total Sales

Gross Domestic Product

Interest Expense over Debt ratio

Dummy variable for the Increase in Foreign Sales

Subindustry of the company

Variable describing the different internationalization measures

Intangible Assets

Long-term Debt over total Assets ratio

Location, country of the company

XI

NSI

PROD

ROA

ROE

S

SDA

SIZE

TASI

TDA

TNI

WSE

Network Spread Index

Productivity

Return On Assets

Return On Equity

Sales

Short-term Debt over total Assets

Size of the company

Transnational Activities Spread Index

Total Debt over total Assets ratio

TransNationality Index

Wages and Salaries per Employee

XIII

LIST OF TABLES

TABLE 1. Summary of the regression results for the TNI and TASI indices. ............................................ 39

TABLE 2. Summary of the regression results for the separate "sales", "assets" and "employees" terms.

............................................................................................................................................................... 39

TABLE 3. Summary of the regression results for the profitability: FSTS-based measures. ..................... 42

TABLE 4. Summary of the regression results for the profitability: TNI- and TASI-based measures. ....... 43

TABLE 5. Summary of the regression results for the total debt ratio TDA. ............................................. 46

TABLE 6. Summary of the regression results for the long-term debt ratio LDA. ..................................... 47

TABLE 7. Summary of the regression results for the short-term debt ratio SDA. ................................... 47

TABLE 8. Summary of the regression results for the drivers: regression coefficients for the European

subsidiaries. ........................................................................................................................................... 50

TABLE 9. Summary of the regression results for the drivers: regression coefficients for the extrapolated

measures. .............................................................................................................................................. 51

TABLE 10. Summary of the regression results for the drivers: regression parameters. .......................... 51

XV

LIST OF FIGURES

FIGURE 1. EU-28 and EA-19 Construction output, 2000-2015, monthly data, seasonally and working

day adjusted (2010=100), taken from Eurostat (2015). .......................................................................... 9

FIGURE 2. EU-28 Total construction, buildings and civil engineering, 2000-2015, monthly data,

seasonally and working day adjusted (2010=100), taken from Eurostat (2015). ................................... 9

FIGURE 3. Profitability analysis if the internationalization process, taken from Lu and Beamish (2004).

............................................................................................................................................................... 13

FIGURE 4. Domestic countries of the sample companies. ....................................................................... 29

FIGURE 5. Cumulative percentage of parent companies in function of the amount of subsidiaries they

control for the different size categories. ............................................................................................... 32

FIGURE 6. Cumulative percentage of parent companies in function of the amount of subsidiaries they

control for the different subindustries. .................................................................................................. 32

FIGURE 7. Percentage of mother companies investing in at least one subsidiary in each of the different

categories. ............................................................................................................................................. 33

FIGURE 8. Average amount of subsidiaries of firms investing in subsidiaries in each of the different

categories. ............................................................................................................................................. 34

FIGURE 9. Geographical distribution of subsidiaries. .............................................................................. 36

FIGURE 10. Percentage of mother companies investing in at least one subsidiary in each of the

different UN subregions. ....................................................................................................................... 36

FIGURE 11. Average amount of subsidiaries of firms investing in subsidiaries in each of the different

UN subregions. ...................................................................................................................................... 37

1

1. INTRODUCTION AND RESEARCH QUESTIONS

During the colonial era, contractor and engineering services were amongst the first to be

internationalised. Over the years, the international presence of these services has continued to grow.

Architects, engineers and contractors (also called the AEC sector) who are active in foreign countries

are no longer an exception. Companies involved in these construction activities are largely

dependent on interim payments, a consequence of their strong need for working capital due to

lengthy project durations. During the global financial crisis of 2008 many AEC companies had

difficulties finding financial resources to fund this working capital need. Numerous small contractor

firms, architectural offices and engineering consultancies went bankrupt, exposing the vulnerability

of the sector to external risks. Internationalization on the other hand is characterised by many

uncertainties, each bearing additional risks for the internationalising firms. The standard financial

vulnerability of these companies combined with these risks of internationalization calls for further

investigation of the effects internationalization has on these firms. With profitability and leverage

this master’s dissertation will focus on two of the most decisive aspects for AEC companies.

Notwithstanding the risks associated with internationalization, plenty AEC firms still engage in

international activities. To get a better understanding of why some companies perform better on the

global market than others, the drivers of internationalization will be discussed in this thesis as well.

The question of how internationalization affects the profitability and the indebtedness of firms has

already received extensive attention from researchers. The drivers of internationalization on the

other hand have also received their share in the existing research. However, most of this research is

of a qualitative nature. In order to get a better feel of these relationships this dissertation will focus

on the quantitative aspects.

For this research the degree of internationalization is approximated through the assessment of

company subsidiaries since most companies do not disclose export data and subsidiary data are

more readily available. The export percentage, percentage of foreign assets and percentage of

foreign employees are approximated by using subsidiary data. Despite its limitations, this working

method also allows for other interesting viewpoints. For the few companies which published their

export data for some years, the relation between these export numbers and their subsidiary data is

assessed to get an idea of how well the subsidiary method approximates the real situation. This

comparison proved that the subsidiary-based approach is reasonably well at approximating the real

export percentage for the sampled AEC companies with available export data.

2 1. INTRODUCTION AND RESEARCH QUESTIONS

Empirical study further showed that the lion share of subsidiary investment by European AEC

companies is still limited to the home country of the parent company and the fellow EU member

states.

The profitability of AEC companies was found to comprise three phases. In a first phase, low

experience with internationalization leads to lower profits. Later, companies get more acquainted

with foreign business and profits increase. Yet at high levels of internationalization coordination

becomes costly, eroding the profits in the process.

The capital structure of AEC companies changes with their reliance on international business. The

findings of the indebtedness regressions show that for the sampled AEC companies higher levels of

internationalization are associated with lower total and long-term debt ratios. Furthermore the data

showed that the relation between short-term debt and internationalization is characterized by a U-

shaped function.

The examination of what drives AEC firms to focus more on international business uncovered some

influences. Company size has a positive influence, as well as increased foreign sales in combination

with decreased domestic sales. Furthermore, the internationalization process occurs in stages of

higher and lower interest in internationalization. Lastly, age proved to have a mixed influence as well.

To my knowledge this master’s thesis is the first to numerically asses the internationalization drivers

and effects of internationalization on the profitability and leverage of European AEC firms in the

period before and during the Global Financial Crisis through their subsidiaries.

The remainder of this master’s thesis is structured as follows. The next chapter aims to provide an

overview of how the world has grown into the globalised economic environment of today and how

AEC companies cope with it. In chapter 3, previous research and the existing literature are reviewed

to come to hypotheses about the drivers of internationalization and its effect on the profitability and

the indebtedness of internationalizing AEC companies. Chapter 4 discusses the methods used for this

research and the data which were used. The results of this study are examined in chapter 5 and

chapter 6 gives an overview of the results and their implications, while trying to address the

limitations and suggesting ideas and key considerations for future research.

3

2. THEORETICAL BACKGROUND

Evolution of the international economic environment

Internationalization is no new phenomenon. Wilkins (2001?) points out that already in 2500 BC

Sumerian merchants realized that they needed contacts abroad to optimize their trade activities.

Later Italian bankers in the thirteenth century brought the internationalization principle to Europe

(Cameron&Bovykin, 1991; Wilson, 1976?). In the colonial era, the English East India Company and the

Dutch East India Company are examples of businesses expanding their activities to other continents.1

The parent enterprises of multinationals before the nineteenth century were traders, bankers or

individual investors. These parent enterprises invested directly in manufacturing, plantations and

mining activities, but no company used their foreign interests on a large scale to sell or manufacture

its products abroad. It was not until the industrial revolution, with the advancements in

transportation and communication, that businesses could truly organise their foreign interests.

With the increasing internationalization came the need for generally accepted rules and standards.

Copeland (2005), De Clercq (2013) and Salvatore (1998) all give a good overview of the evolution of

the monetary system in the western countries. In the 1870s the system of the gold standard was

introduced to regulate international currencies. According to this system currencies were valued

through parity in gold. After the First World War most countries changed to the system of the gold

exchange standard in which the value of domestic paper was guaranteed by gold and gold exchanges

(currencies which could be traded for gold). After the economic crisis of 1929 which was

characterised by increasing devaluations and protectionism, the system of Bretton Woods was

introduced in 1944. The mutual parities among the participating countries were fixed and the dollar

was taken as gold exchange. Each participating country committed to intervene with dollars should

their parity deviate more than 1% of the dollar parity. The United States of America guaranteed the

exchange between dollars and gold. The International Monetary Fund (IMF) was established to

ensure the compliance of the rules of the system. Eventually there were so many dollars in foreign

hands that the U.S.A. could no longer guarantee the convertibility into gold and the system ceased to

exist in 1971. From then on, most countries switched to a system of floating exchange rates.

1 Researchers still disagree whether to consider trade companies from the colonial era as multinational enterprises or not

because there were not yet nation-states at the time.

4 2. THEORETICAL BACKGROUND

Together with the dollar parity, the Bretton Woods convention also laid the groundwork for

agreements on free trade (Hoekman&Kostecki, 1995; Wouters&De Meester, 2007). For this purpose

an International Trade Organisation (ITO) would be instituted but this was never effectuated.

However the participating countries agreed on a General Agreement on Tariffs and Trade (GATT)

which in 1995 after the Uruguay Round was substituted for a real international organisation, the

World Trade Organisation (WTO) (Rugman&Collinson, 2006). It was also in this Uruguay Round

(1986-1994) that international agreements were reached on services, intellectual property rights,

dispute settlement procedures and agriculture.

Devroe and Wouters (1996) give an overview of the institutions which were formed in Europe to

improve economic integration and which ultimately led to the creation of the European Union. The

European Union member states have a common turbulent history. After the Second World War, the

European nations chose to enhance political, economic and monetary integration. In 1951 Belgium,

France, Italy, Luxembourg, the Netherlands and West-Germany founded the European Coal and Steel

Community (ECSC) with the Treaty of Paris to create a common market for coal and steel to

neutralise competition between the European powers over the natural resources at their borders

(Devroe&Wouters, 1996). It was the first supranational organisation and became the start of further

economic integration in Europe. Six years later, these countries signed the Treaties of Rome in 1957

to form the European Atomic Energy Community (EURATOM) and the European Economic

Community (EEC). In 1960 the European Free Trade Agreement (EFTA) was signed by Austria,

Denmark, Great Britain, Norway, Portugal, Sweden and Switzerland to form a common market. In

1992 the countries of the EEC and the EFTA founded the European Economic Area (EEA) for the free

movement of persons, goods, services and capital between the member states. Following the

Maastricht Treaty in 1993 the member states of the EEC - which was also renamed European

Community (EC) - agreed on the formation of a political, economic and monetary union. The

European Union (EU) was born. In 2002 the euro was introduced in 12 of the European Union

member states. Most recently, Croatia joined the EU in 2013, bringing the total number of member

states at 28, with 19 member states in the eurozone and several other countries queuing for

membership.

In 2008 a global financial crisis crushed economic growth in most of the western economies (Crotty,

2009; Peersman&Schoors, 2012). It was triggered by the bursting U.S. housing bubble and led to a

liquidity crisis wherein many banks and financial institutions were threatened, only to survive with

help from the national governments. The crisis also caused the European debt crisis in which Greece,

Portugal, Ireland, Spain and Cyprus came in financial trouble and needed help from the other

eurozone countries, the IMF and the ECB (European Central Bank) (Peersman&Schoors, 2012).

5

Consumption and production decreased and companies had more and more trouble financing their

activities (Peersman&Schoors, 2012). Another consequence of the crisis was that large construction

projects got delayed or cancelled, which put even more pressure on the financial situation of

businesses in the construction industry (Peersman&Schoors, 2012).

The process of business internationalization

Despite all risks associated with internationalization many companies decide to venture into the

endeavour internationalization can be. Yip (2003) states there are four main types of drivers: market

drivers, cost drivers, government drivers and competitive drivers. Market drivers facilitate market

expansion, for example if the foreign market has similar customer needs or when the same

marketing can be used. When pursuing cost reduction, scale economies can convince companies to

go abroad. Geographic separation of technology intensive activities and labour intensive activities

can be another cost argument for internationalising. Foreign governments can also attract expanding

firms by easing the international growth process by standardising technology, promoting free trade

and reducing barriers to trade and investment. Finally, competitive drivers relate to more defensive

approaches. Competitors who are globalised might enjoy more benefits than an enterprise which

focuses solely on the domestic market. Preventing the transfer of negative effects on one foreign

market to another foreign market of the company also encourages an integrated international

approach.

Frequently internationalization is confused with globalisation (Rugman, 2005). However,

internationalization of a company is often limited to a region instead of a global scope. The triad

regions of Northern America, Western Europe and Japan account for the largest portion of the

international trade. Rugman and Collinson (2006) mention that in spite of the globalisation trend all

around the world, intraregional trade is still subordinate to interregional trade.

The economic integration of the EU member states has stimulated companies to import and export

more with each other. Spithoven and Teirlinck (2005) state that firms in smaller countries, like many

of the EU member states, are also more likely to enter foreign markets given the limited size of their

domestic markets. On the other hand, the union is not yet completely integrated in the legal and

fiscal domain. This makes it interesting in a way for companies to establish subsidiaries and other

facilities in other member states with better conditions for the enterprise. However, the economic

interdependency of the European Union members and the lack of an integrated financial approach

have contributed to the gravity of the global financial crisis of 2008 in Europe (Peersman&Schoors,

2012).


Which foreign market a company should choose depends on the match between the firm and the

host country. The distance between the enterprise and the host country is described by the CAGE

framework (Ghemawat, 2007; Johnson, Whittington, Scholes, Angwin, &Regnér, 2014). This distance

is composed of four dimensions: Cultural, Administrative, Geographical and Economic. Differences in

language, religion, ethnicity and social norms are cultural barriers to smooth operation of the firm in

the foreign country. Similarities in political or legal traditions decrease the administrative distance.

The geographical distance refers not only to the physical distance, but also to other country

parameters like infrastructure and size. Lastly, the economic dimension involves the unequal

distribution of wealth among nations. One should bear in mind that not all markets in an interesting

host country are a good choice themselves. For example, Mali has close cultural and administrative

ties with France. However, the majority of the Malian population cannot afford luxury products a

French firm might be focusing on. Porter’s five forces framework (Porter, 2008) and the well-known

PEST(EL) framework (Johnson et al., 2014) can further help deciding if the market one is screening is

a suitable option.

The path to international expansion is different for each firm. Firstly, not all firms have an equal

amount of experience before internationalizing (Jones&Young, 2009?). A first group of firms has

been active on an international scale since the firm was founded; these firms are ‘born global’. Other

firms gained experience in the domestic market before expanding abroad.

Secondly, the followed strategy differs among companies. Johnson et al. (2014), Kotabe (2001?),

Tallman and Yip (2001?) mention three different entry modes, in increasing order of resource

commitment: exporting, collaboration and foreign investment. Johanson and Vahlne (1977) mention

that firms usually start exporting through an agent. Later sales subsidiaries are established and finally

in some cases overseas production units are created. Firms who limit their foreign expansion to

exports mainly focus on serving new markets. In some cases, firms wish to collaborate with foreign

partners who have more experience with the local market; other motivations are for instance

licensing and franchising. Collaboration is also common when firms are seeking to expand their

resources or to broaden their knowledge. Increasing production efficiency and gaining more power

over suppliers and buyers are other arguments for collaboration. In the most comprehensive case

companies even invest in joint ventures or subsidiaries. By investing in foreign equity these firms

have complete control over their international interests

Foreign direct investment (FDI) is the term used to refer to equity funds invested in foreign countries

(Ghemawat, 2007; Helpman, Melitz, &Yeaple, 2004; Rugman&Brewer, 2001; Rugman&Collinson,

2006). The investments can range from variable costs of foreign affiliates, e.g. transport costs and

7

tariffs, to the foundation of new foreign subsidiaries and corporate branches. However, the concept

mostly refers to the ownership of foreign companies only. FDI occurs mostly between industrialised

countries and sometimes from developed nations to newly industrialised and less developed

countries. However, global FDI dropped heavily at the start of the global financial crisis and for the

first time ever, more FDI was invested in emerging countries than in industrialised countries (Kekic,

2009).

Which entry mode is preferred and whether or not the firm has been international from the start all

depend on several factors. A lot depends on what the company is seeking. Besides sensible reasons

mentioned above, the choice may also be motivated by a bandwagon effect (Steinmann, Kumar,

&Wasner, 1980) or by personal ambitions of the management (Dichtl, Leibold, Köglmayr, &Mueller,

1984; Leonidou, Katsikeas, &Piercy, 1998). The internationalization process may be intentional or

may be an unplanned result of existing customer relationships and current activities (Bell, 1995;

Loane&Bell, 2009?). Jones and Young (2009?) also point out the implications of the chosen entry

mode on the organisation, the strategy, the legal field and the accounting field. Plakoyiannaki and

Deligianni (2009?) and Karafyllia (2009?) report interrelations and growth and learning spillover

effects from the foreign to the domestic market. For example threats and weaknesses in the

domestic market might be counteracted by strengths and opportunities in the foreign market.

Rugman and Collinson (2006) further noticed that companies also engage in internationalization to

counter their competitors. In this situation they start up operations in the domestic country of the

competitor to take advantage of the competitors’ moved focus.

A special case of international companies are the multinational enterprises (MNE’s). Westney and

Zaheer (2001?) define a multinational enterprise as a multi-country organisation that maintains

multiple units operating in multiple environments. There is no consensus about a required amount of

countries where the MNE must be active in. Scott (1992) further emphasizes the following:

“Their [MNE’s] central management is confronted with the challenge of designing systems that retain

sufficient unity and coherence to operate as a common enterprise and, at the same time, to allow

sufficient latitude and flexibility to adapt to greatly varying circumstances.” (Scott, 1992, p. 138)

Research in Great Britain (Grant, 1987) and in the U.S.A. (Hitt, Hoskinsson, &Kim, 1997) has further

shown that enterprises with unique resources and competencies become more profitable when

going abroad. An increased international presence also gives MNE’s the advantage of access to a

broader base of financial markets (Tallman&Yip, 2001?).


AEC industry

Until the nineteenth century, there were no separate professions regarding construction,

architecture and structural engineering (Linder, 1994). All professions were included in the

profession of master builder. This person was responsible for the design, elaboration and execution

of construction projects. Nowadays all these activities can be viewed as separate sub-sectors in the

construction industry. The European Union has defined them by the following NACE codes:

- F.41: the construction of buildings by contractors and developers

- F.42: the construction of infrastructure, utility projects, water projects and other

civil engineering projects

- M.71.1.1: architectural activities

- M.71.1.2: engineering activities and related technical consultancy

Recent trends have however blurred the boundaries between the professional activities with clients

preferring a more integrated approach, for example design-and-build projects (Osegowitsch, 2007?).

Companies engaging in the execution of construction projects require a lot of investment in fixed

assets and working capital. Not all contractors are endowed with the same levels of fixed assets and

working capital. This implies that for larger projects, some contractors do not possess the necessary

resources to execute these projects. In Belgium for example, contractors are classified in different

categories according to the size and kind of projects they can handle (Belgian Federal Government,

2013).

Firms charged with the design and calculation of structures are more knowledge intensive. For these

companies the education and experience of their employees, as well as customer relations, are the

sources of their success. Rugman (2005) further mentions that knowledge intensive service firms, like

architecture and engineering companies, are largely location bound. This means that these firms’

activities take place in their domestic country or a country in the proximity of this country and that

most of these companies do not engage in business activities at the other side of the planet.

The construction sector in the EU-28 accounts for more than 5% of the (gross) value added.

Construction of buildings accounts for around 78% of total construction and civil engineering works

make up the other 22% (Eurostat, 2015). The European construction industry was hit significantly by

the global financial crisis as illustrated by figure 1 (Eurostat, 2015). The crisis crushed construction in

the euro area a bit harder than in the EU members which did not use the euro as their currency. The

recovery of the construction sector in these countries is also characterized by a somewhat more

arduous process.

9

FIGURE 1. EU-28 and EA-19 Construction output, 2000-2015, monthly data, seasonally and working day adjusted (2010=100), taken from Eurostat (2015).

FIGURE 2. EU-28 Total construction, buildings and civil engineering, 2000-2015, monthly data, seasonally and working day adjusted (2010=100), taken from Eurostat (2015).


Chor and Manova (2012) further found that due to the global financial crisis, firms which depend

substantially on external financing, like the construction industry, decreased their exports

considerably.

As shown by figure 2 not all construction subindustries faced the same difficulties in Europe

(Eurostat, 2015). Since civil engineering projects produce common goods, the demand for the civil

engineering sector remained relatively stable.

The construction sector in the Baltic countries suffered the most. In 2009 the growth rate of the

Lithuanian construction industry reached a low of -54.5% (Eurostat, 2015). The largest drop in growth

rate in the civil engineering works after the global financial crisis occurred in Greece in 2011: -39.8%

(Eurostat, 2015). On the other hand there were countries, for example Germany and Austria, in

which the growth rates remained relatively stable.

11

3. LITERATURE REVIEW AND HYPOTHESES

Profitability

Grant (1987) studied the influence of internationalization on the performance of British

manufacturing industries over the period 1972-1984. He used three different measures to quantify

profitability and found a positive linear relation for all cases indicating that a larger market scope,

geographical risk spreading and the use of certain intangible assets on the global market contribute

to a higher profitability. Vernon (1971) verified this relation as well. Hamel and Prahalad (1985),

Kogut (1985), Porter (1985) and Rugman (1981) also state that a larger geographical scope provides

additional benefits in the form of economies of scale, experience and scope.

Horst (1972) and Shaked (1986) however found that domestic firms are more profitable than

multinational corporations. Siddharthan and Lall (1982) also obtained a negative linear relation for

American manufacturing firms between 1976 and 1979. They attribute the negative relation to

increased competition on the global market compared to the domestic market. Ruigrok, Amann and

Wagner (2007) studied the Swiss manufacturing industry from 1998 to 2005 and tested multiple

relationships between internationalization (measured in export percentage) and profitability. For

their sample they found a negative linear relationship as well.

H1.1.A:For AEC companies there exists a positive relation between internationalization and

profitability.

H1.1.B:For AEC companies there exists a negative relation between internationalization and

profitability.

Other authors have found nonlinear relations between the internationalization level and profitability,

using both different measures for profitability and for internationalization. Capar and Kotabe (2003)

found a U-shaped relation for German service companies. Contractor, Kundu and Hsu (2003) came to

the same conclusion in their study of 204 large service firms. Ruigrok and Wagner (2003) found the

same relationship for German manufacturing firms in the period 1993-1997. They suggest that firms

have to go through an organizational learning process with less profit allowing them to perform

better at higher internationalization levels.

12 3. LITERATURE REVIEW AND HYPOTHESES

Multiple researchers however obtained an inverted U-shaped relationship (Geringer, Beamish,

&daCosta, 1989; Gomes&Ramaswamy, 1999; Hitt et al., 1997). The costs associated with further

internationalization increase progressively and at high internationalization levels, the benefits from

additional internationalization no longer compensate for these costs. Companies with an extreme

degree of internationalization will perform worse than firms with a moderate level of

internationalization. Ruigrok et al. (2007) tested the cubic relation as well in their research on Swiss

manufacturing firms and found it to be an inverted U-shape too.

H1.2.A: The relation between internationalization and profitability for AEC enterprises is

characterised by a quadratic relation with a minimum.

H1.2.B: The relation between internationalization and profitability for AEC firms is characterised

by a quadratic relation with a maximum.

Since there is no consensus among researchers, there are probably truths on both sides. This led

researchers to the idea that the dependence of performance on internationalization is not

characterised by a quadratic relationship, but by a cubic relation in three phases (figure 3) with both

a minimum and a maximum (Lu&Beamish, 2004). This cubic S-shaped relation can partially explain

the contradictory findings among the researchers of the linear relations. Depending on the phase in

which the studied companies are located, the firms become more or less profitable by concentrating

more on overseas trade (Contractor et al., 2003). From this point of view the contradictory

conclusions of the research on the quadratic relation can all be unified as follows. When a company

first starts the internationalization process, the firm will have many development costs – mainly

related to new market entrance and to the international aspect – while the benefits are not yet

visible. In the second stage, the company will be more integrated in the globalised environment and

will start reaping the benefits leading to a higher performance. On the other side, if a firm fixates too

much on foreign markets the coordination costs of further internationalization accelerate while the

benefits start decelerating. This results in a lower performance for extreme levels of

internationalization.

Geringer et al. (1989) confirmed this horizontal S-shaped relation for the 100 largest multinationals in

the U.S.A. and Europe. Riahi-Belkaoui (1998) focused on large American enterprises between 1987

and 1993. Contractor et al. (2003) tested the same hypothesis for 204 service companies across 14

service sectors. Lu and Beamish (2004) examined the performance of 1,489 Japanese firms in the

period 1986-1997. Ruigrok et al. (2007) finally studied the profitability of Swiss manufacturing firms.

13

All came to the same conclusion: the dependence of profitability on internationalization can be

characterised by three phases as depicted in figure 3.

H1.3: The relation between internationalization and profitability for AEC firms is characterised

by a cubic relation with a minimum at lower values and a maximum at higher amounts

of internationalization.

FIGURE 3. Profitability analysis if the internationalization process, taken from Lu and Beamish (2004).


Indebtedness

Singh and Nejadmalayeri (2004) analysed 90 French firms over 4 years and found that the degree of

internationalization had a significant effect on the indebtedness of the companies. They expressed

the level of internationalization of companies through the export percentage. The relation for short

term debt is characterised by an inverted U-shape. The relations for long term and total debt are

both positively and linearly related to internationalization.

This positive linear relation might seem odd considering the higher financial and political risks

associated with internationalization. Chen, Cheng, He and Kim (1997) state that on average

multinational firms have lower debt ratios than purely domestic companies. On the other hand, the

risks of depending on foreign markets are spread if the foreign activities are geographically

diversified (Heston&Rouwenhorst, 1994; Lewellen, 1971). Burgman (1996) could not confirm this

effect in his study of public American firms between 1987 and 1991. Reeb, Mansi and Allee (2001)

confirmed that international firms face a lower cost for debt financing.

H2.1: AEC firms which expand their foreign activities become more leveraged.

H2.2.A: AEC companies which expand their foreign activities carry more long-term debts.

The nonlinear relationship in the case of short term debt is explained by Singh and Nejadmalayeri

(2004) as the result of the risk aversion of banks. In the early internationalization stages investors

perceive high information asymmetries, decreasing the chance to get a long-term debt. This

necessitates the need of short-term debt financing. In later stages of expansion when the firms have

already proven their success, investors will face lower risks and long-term debts will become more

easily available to the internationalising firm. Reeb et al. (2001) state that more internationally active

firms receive better credit ratings than companies with a smaller international scope, implying that

the international firm will find the financial sources they prefer, more easily.

H2.2.B: Up to a certain degree, internationalising AEC firms finance their activities with more

short-term debts. But when these companies have earned a larger foreign presence,

they focus on other financial sources.

15

Drivers of internationalization

It was not until recently that researchers started focusing on the drivers of internationalization rather

than its effects. Todo (2011) was the first to quantitatively assess drivers of internationalization with

his study on Japanese firms over the period 1997-2005 for which he found some contributing factors

to internationalization. Since not many researchers have followed his example, Todo’s work will be

the reference used for this subject.

Size, productivity, indebtedness and behaviour of similar firms all contributed to a certain extent.

Size plays a role in that larger firms can realize scale effects more easily than their smaller

competitors. More productive firms have fewer difficulties overcoming the entry costs associated

with exploiting new markets. On the other hand financially constraint enterprises have a harder time

supporting cross-border trade. The attitude of companies in the same industry and region can also

cause spillover effects. Nonetheless, Todo (2011) learned that the influence of these factors is only

limited.

The principal influences Todo (2011) determined, are their internationalization behaviour in the

previous year and undetermined heterogenic firm characteristics. More specifically he found that

companies which do not engage in international activities are very probable to remain domestic the

next year. Similarly, firms involved with export or investing in foreign interests are highly inclined to

continue these respective activities. Todo (2011) took some firm characteristics into account, but the

unspecified heterogenic traits contained in his random intercepts proved to be an important

determinant in explaining the internationalization behaviour of companies.

Besides the work of Todo (2011), other researchers had already discovered some relations, although

not with the intention of quantifying drivers of internationalization directly.

Mayer and Ottaviano (2007) state that the largest portion of a country’s export is driven by a few

large companies only. Many other researchers also concluded that size has a significant positive

effect on business scope (Cavusgil, 1984; Ogbuehi&Longfellow, 1994; Tookey, 1964). This finding is

also consistent with Todo’s results. A large firm size is a sign of good performance in the foregoing

years. Larger firms gather more income from export activities, while smaller companies are more

confined to their domestic markets. This is partly because smaller firms see exporting as a risky

undertaking (Burpitt&Rondinelli, 2000). Larger firms have a larger buffer in case the overseas

business turns awry.

H3.1: Larger AEC firms focus more on foreign business.


Financial constraints are a well-known restriction to global expansion. Minetti and Zhu (2011),

Miyakawa et al. (2013), Ogbuehi and Longfellow (1994) and Todo (2011) all found a negative relation

between financial limitations and export and FDI activities. On one hand, financial institutions are

less likely to grant credit to companies which already carry lots of debt while internationalizing firms

mostly need additional financial resources. On the other hand, the firms themselves might also

prefer not to increase their foreign activities when carrying a lot of debts. Especially if the enterprise

is in financial trouble, it probably does not want to get involved in additional internationalization

risks.

Burgman (1996) states however that a more leveraged capital structure can be used to hedge

political and exchange rate risks, promoting entrance into new overseas markets. As long as

companies generate more income than the financial expenses they face, leverage endorses higher

profits for the same amount of invested equity. This point of view looks at the risks of

internationalization as opportunities. From a purely financial point of view, access to a broader

capital market also allows for enterprises to restructure their capital structure with financial sources

on better conditions.

H3.2.A: AEC companies with many financial obligations focus less on international expansion.

H3.2.B: AEC enterprises with many financial obligations focus more on international expansion.

Helpman et al. (2004), Melitz (2003) and Miyakawa et al. (2013) all agree with Todo (2011) that a

high productivity is a strong precondition to hurdle the entry costs. Bernard and Jensen (1999)

conclude that firms with a good performance have a higher chance of starting export activities. They

also state that firms will start exporting in more productive times, while they will stop exporting

when their performance declines. Highly productive firms might also choose to expand abroad to

increase the scale of their profits. On the other hand, when business turns bad they might decide to

decrease this scale.

H3.3: AEC firms with a higher productivity will engage more frequently in cross-border

expansion.

Todo (2011) mentioned that companies whether domestic, exporting or engaging in foreign direct

investment stay in the same category. Domestic firms will stay domestic, while their exporting

17

counterparts and firms investing in transnational trade will continue to do so. Bilkey and Tesar (1977)

also found that export success of enterprises in the preceding years is a strong motivator for taking

the next step in their international expansion process. Companies expect that this positive trend can

be capitalized by investing more resources into their foreign activities. Cavusgil (1984) found that the

combination of dissatisfactory domestic sales and beneficial foreign sales might enhance this effect.

H3.4.A: AEC firms keep on following the same international strategy as the year before.

H3.4.B: An increase in foreign sales of AEC companies in the foregoing years endorses expansion

of the foreign activities.

H3.4.C: A decrease in domestic sales of AEC companies combined with an increase in overseas

sales also favours a stronger cross-border presence.

Mayer and Ottaviano (2007) point out that big differences exist between industries. Some industries

face few barriers to foreign expansion, whereas other industries are very sensitive to the culture,

geographical distance or government and legislation related issues.

Some industries face more competition than others, urging them to keep up with their competitors

or even surpassing them. With some managers this triggers a bandwagon effect resulting in copy

behaviour (Johnson et al., 2014) with companies increasing their foreign presence just because the

competition does the same.

H3.5: The subindustry in which an AEC enterprise is active has an influence on its

internationalization behaviour.

Koenig, Mayneris and Poncet (2010) studied geographical spillover effects in France and concluded

that the export decision, though not the export intensity, is influenced by the behaviour of

geographically close enterprises. Todo (2011) obtained similar findings but only considered the

export decision.

Companies from the same country often share same cultural aspects as well which influence

internationalization (Ghemawat, 2007; Johnson, Whittington, Scholes, Angwin, &Regnér, 2014). Risk

aversion, entrepreneurship and corporate culture are examples of this. Enterprises originating from


small countries are also more likely to expand their businesses to other countries because of the

small size of their domestic market (Spithoven&Teirlinck, 2005).

H3.6: The country from which an AEC company originates, has an influence on its

internationalization behaviour.

Autio, Sapienza and Almeida (2000), Casulli (2009?) and Reuber and Fischer (1997) point out that

business networks, customer relations (domestic and foreign), learning intensity and knowledge

about the foreign market are important factors which determine the success of the foreign

expansion operation. Knowledge about the foreign market further depends to a large extent on the

experience of the management staff and on proper preparation.

Related to these intangible assets are the notions of human, structural and relational capital (Bontis,

1998; Bontis, Chua, &Richardson, 2000; Tsai&Ghoshal, 1998). Human capital refers to the intellectual

value of a company’s employees. It comprises not only the education and experience of the

individual employees, but also work ethics, attitude and experience. Since these assets are difficult to

quantify and employees are not ‘owned’ by an enterprise, there is no unique generally accepted way

of measuring human capital. Employee salaries and wages, employee tenure and level of education

are examples of measures which are all applied interchangeably in the literature.

The structural capital of a firm relates to the non-human sources of intellectual capital in a company.

They are the result of the business activities and are related to the experience inherent to the whole

enterprise. Examples of structural capital are work routines, international experience and databases.

Innovation and efficiency are also related to the structural capital notion.

Lastly, relational capital encompasses the value of customer relations and marketing channels. The

value of a customer base, a distribution network or perception by the market is difficult to quantify.

Therefore this aspect of intangible capital can also be assessed by various measures, as there are

market share, average sales per customer and customer satisfaction.

International experience, both on the company level and the employee level, as well as foreign

customer relations and strategic attitude are all included in these intangible capital aspects.

H3.7: AEC companies with more human, structural or relational capital are more likely to

increase their commitment to international activities.

19

4. RESEARCH METHOD AND DATA

Data

The data for this study are collected from the Amadeus database of Bureau Van Dijk. This database

contains financial data for over 19 million European enterprises. The period for which data are

retrieved is 2003-2011. For this study, the results are limited to companies with a European Union

member state as their domestic country in 2003. The selection is further confined to firms in the

following four industries: construction of buildings and development of building projects (Nace F.41),

civil engineering (Nace F.42), architecture (Nace M.71.1.1) and engineering and technical consultancy

activities (Nace M.71.1.2). In order to focus on real businesses, private limited companies and similar

constructs are excluded by selecting only companies with at least 10 employees. Another selection

criterion is the control over foreign subsidiaries. As mentioned before, both export and FDI are

approximated by using the sales and possessions of foreign subsidiaries. Only firms with full control

over at least one foreign subsidiary and with consolidated statements are retained. 2 After this

rigorous selection procedure 341 firms remained, originating from 13 of the 15 EU member states as

of 2003. All together, they control 10,923 subsidiary companies.

Method

First the required data are gathered from the Amadeus database of Bureau van Dijk. After data

collection, an empirical study of the relation between mother companies and their subsidiaries is

conducted. Next, the relation between export data and subsidiary data is assessed. After this, the

general descriptive statistics are examined for each subject. Then the regressions discussed before

are realized, applying the variable definitions as discussed in the following sections. All variables are

winsorized (at the 1% and 99% quantiles) and standardized. For all regressions, the method with

fixed effects is adopted to account for unspecified company-specific effects which might otherwise

bias the regression results. As coefficient covariance method, the White standard errors are chosen

2 In order to assess the reliance of the companies on their foreign subsidiaries, the data in Amadeus of the parent

companies have to be based on the consolidated financial accounts.

20 4. RESEARCH METHOD AND DATA

to accommodate for possible multicollinearity or heteroskedasticity. Autocorrelation and

endogeneity are checked manually as well. For the internationalization measures, defined in the next

section, each time both the definitions based on the European subsidiaries and the extrapolated

variations were tested. At the end, the results of the regressions are evaluated and confronted with

the different hypotheses, after which discussion is possible.

Dependent variables

Profitability

Most researchers (Gedajlovic&Shapiro, 2002; Goddard, Tavakoli, &Wilson, 2009; Lincoln, Gerlach,

&Ahmadjian, 1996; Ruigrok et al., 2007; Ruigrok&Wagner, 2003) measure the profitability of a

company with the return on assets (ROA). Some researchers use the return before tax and others use

the earnings before interests and taxes (EBIT). Other measures such as profit margins are based on

sales rather than the total assets. Since international profit, like domestic profit, departs from the

assets which are available to a firm, some measure based on ROA is preferred. For this study, the

return on assets ratio based on the EBIT is chosen. This ratio expresses how efficient the companies

exploit their assets to generate income (Penman, 2013).

Indebtedness

The majority of researchers studying the capital structure or the indebtedness of companies

(Devesa&Esteban, 2011; Frank&Goyal, 2009; Graham&Harvey, 2001) work with the ratio of total

debt over total book value of assets (TDA). Another variation of the classic debt ratio is the leverage

ratio, in which the debt is scaled with the firm equity (Chiang, Chang, &Hui, 2002;

Mahmood&Zakaria, 2007). However, the debt ratio is used more frequently than the leverage ratio

in this research domain. In order to allow better comparison with the existing literature, the standard

debt ratio will thus be used in this study.

Frank and Goyal (2009) further use both a debt ratio based on the book value of assets and a debt

ratio based on the market value of assets. Market values take future growth opportunities into

21

account. On the other hand, financial decisions are usually taken based on book values since market

values are unpredictable and capricious. For this reason, the assets in the indebtedness ratio will be

measured by their book value.

Singh and Nejadmalayeri (2004) also made a distinction between short-term debt and long-term

debt. To measure both contributions to the capital structure, they used the short-term and long-term

debts scaled with the total assets (SDA and LDA). Based on their example, short-term and long-term

variations will be studied for the European construction sector as well.

Degree of internationalization

Different researchers use different measures to quantify internationalization (Ietto-Gillies, 1998;

Preece, Miles, &Baetz, 1999; Rugman, 2005; Spithoven&Teirlinck, 2005; Westhead, Wright,

&Ucbasaran, 2001). The most used aspect of internationalization is the export sales of a company

(Spithoven&Teirlinck, 2005). The FSTS ratio (Foreign Sales over Total Sales) scales the export with the

total turnover of a company. Other important dimensions are the inputs further boosting

internationalization: foreign direct investments (FDI). These include investments in foreign business

divisions, as well as foreign subsidiaries. FDI can be measured with various ratios, taking into account

the assets (FATA), the employees (FETE) or the countries associated with the foreign investments.

In 1995 the UNCTAD (United Nations Conference on Trade and Development) defined the

transnationality index (TNI) as the most representative reflection of the actual internationalization

level. The transnationality index is composed as the mean of the FSTS, FATA and FETE ratios

(Rugman, 2005; UNCTAD, 1995). It can be viewed as a measure for how much a company depends on

the international market for their sales, assets and employees.


Ietto-Gillies (1998) later adjusted this transnationality index to take the geographical spread into

account as well. To achieve a better measure, she added a fourth ratio into the equation: the

network spread index (NSI). Ietto-Gillies (1998) found that the amount of countries in which a firm

invested, relative to the total amount of countries in which foreign investments took place (besides

the home country), had a significant influence. 3

If for example a company would depend for 90% of its sales, assets and employees on foreign trade

(as defined by the TNI) but is only active in one foreign country, this firm is considered less

international than an enterprise which only depends for 20% on international trade but is active in 60

countries. The measure she finally deemed more truthful takes into account both is defined as the

product of the predefined TNI and this NSI. This new indicator is called the transnational activities

spread index (TASI).

Others, like Todo (2011), assess internationalization through dummies distinguishing between

domestic firms, exporters and firms engaging in FDI.

On one side, internationalization brings opportunities like economies of scale and new markets. On

the other hand, it is a risky undertaking. Different governments, currencies and traditions are just a

few of the many risk factors. Risk always has a positive and a negative side. The dependency on the

international market and the risks associated with it can in fact be distinguished in two separate

variables. The dependency of a company on its international activities is reflected in the intensity of a

firm’s international presence, e.g. as measured by the TNI. Risk spreading, on the other hand, can be

approximated by the NSI. Ultimately both aspects of internationalization can also be combined into

the TASI as Ietto-Gillies (1998) did.

3 In theory, one could also take the total amount of countries in the world. However, this number is somewhat arbitrary

since another amount of countries will just change the magnitude of the coefficient in the regressions, but not the sign. For

this study, the total amount of UN member states is chosen: 193 countries.

23

Since companies are not obligated by law to report export numbers and foreign investments in their

financial statements and most firms prefer for this information to remain confidential, these

quantities are mostly not included in the Amadeus database. Only a limited amount of the sampled

companies have entered their export numbers in the Amadeus database.

However the names and locations of the subsidiaries of each company are included. These names

can then be entered into the database. Following this method, the sales, assets and employees of

these subsidiaries can be retrieved. This study tries to assess the driving forces of internationalization

and its effects by using these subsidiary data as an attempt to quantify these internationalization

aspects with less confidential information. Here it is assumed that the subsidiaries do not export

their services outside their own domestic country and that the parent company itself does not offer

its services to foreign clients. It should still be noted that this method only works for European

subsidiaries, since Amadeus does not contain information of companies outside of Europe. However,

it can be observed that a vast majority of the subsidiaries in the sample is located in Europe. The

influence of the firms outside Europe will be approximated by extrapolating the values found for

their European counterparts. For the limited amount of companies which published export data, it is

then tested whether the values based on the European subsidiaries or the extrapolated values for all

subsidiaries is the best approximation of the real export situation.

Implementing these practical adaptations in the aforementioned definitions the FSTS, FATA, FETE

and TNI ratios become:


Another restriction by working with the Amadeus database is that the database does not include the

date of when a company became the subsidiary of another enterprise. Also, subsidiaries do not

always have financial data available for all years in the 2003-2011 period. It is therefore assumed that

in the years for which data are available, the subsidiary company was already controlled by their

current parent enterprise. This also implies that the NSI variable becomes a company-specific

constant in time.

To assess what drives enterprises to expand abroad, only the time-dependent measures will be

tested. This leaves the TNI measure, the TASI measure of Ietto-Gillies (1998) and their composing

terms. The TNI measure quantifies the dependency of the companies on foreign trade, while the TASI

measure combines both this dependency and the geographical risk spreading aspects of

internationalization. Because this study focuses on the change in internationalization behaviour, the

percentage change of these internationalization measures is used.

Since the drivers of NSI separately cannot be quantitatively assessed, an empirical study is executed

as well to get a better view on the geographical choice for locating a subsidiary.

25

Independent variables and estimation models

Profitability relation

Following the example of Ruigrok et al. (2007) and Ruigrok and Wagner (2003) the degree of

internationalization is measured as the ratio of foreign sales over total sales (FSTS). For this study,

the ratio is adapted to the subsidiary working method though. Besides the FSTS† also (FSTS†)² and

(FSTS†)³ are implemented. For the cubic term a negative sign is expected, but for the linear and

quadratic terms no straightforward expectations could be determined. Since subsidiary sales alone

did not prove to be the best approximation for total export sales, the TNI and TASI ratios are tested

as well.

Besides these ratios, other control variables are implemented. The reliance on intangible assets (ITA)

is measured by their percentage in the total asset structure of the enterprise. Different researchers

found a positive effect of intangible assets on profitability (Lu&Beamish, 2004; Yip, Biscarri, &Monti,

2000) and construction project success (Chua, Wang, &Tan, 2003; Han, Kim, &Kim, 2007; Javernick-

Will&Levitt, 2010; Khanna, Palepu, &Sinha, 2006; Orr, 2005). The industry and location effects are

included by the average ROA for each subindustry (ROA_IND) or each country (ROA_LOC) in each

year. Both are expected to have a positive influence on firm profitability. The firms are grouped in

four industry categories, according to their NACE codes: F.41, F.42, M.71.1.1 and M.71.1.2. The

standard practice of measuring firm size (SIZE) is the log of the total assets (Goddard et al., 2007).

The indebtedness, measured as total debt over total assets (TDA), is used to give an approximation

for financial risks. The age (AGE) of the enterprises is expressed in the amount of years since their

incorporation. The literature is in general still undecided on the influence of age, size and

indebtedness. The effects of the global financial crisis of 2008 are isolated in the variable CRIS.

Following the example of the International Monetary Fund (IMF), the World Bank and other financial

institutions, this variable is defined as the average GDP growth rate in the country of origin of the

mother company in each year (World Bank, 2015). This GDP growth is of course expected to be

positively related to company profits. Company-specific effects are included in the cross-sectional

fixed effects.


Integrating all variables defined above into a regression model gives the following equation:

With INT = FSTS†, FSTS†ep, FSTS† x NSI†, FSTS†

ep x NSI†, TNI†, TNI†ep, TASI† or TASI†

ep

Indebtedness relation

The study of the indebtedness follows the method of Singh and Nejadmalayeri (2004) and adds a few

contributions of other authors (Chen et al., 1997; Reeb et al., 2001; Titman&Wessels, 1988). The

main focus is on the degree of internationalization. This degree of internationalization is measured

both with the TASI and TNI coefficient, since the literature uses different internationalization

measures and the indebtedness not only depends on the company sales, but also on the firm’s

investment in assets and the cost of employees. Besides the linear forms, the quadratic forms (TASI²

and TNI²) are implemented as well for the short-term debt as Singh and Nejadmalayeri (2004)

foresaw. Regarding the internationalization coefficients, both the value based on the European

subsidiaries (TASI† and TNI†) and the extrapolated values (TASI†ep and TNI†

ep) are tested. In line with

Singh and Nejadmalayeri (2004) and other authors, a positive relation is expected between the linear

terms and all included debt aspects and similarly a negative sign is predicted for the quadratic terms

in the short-term debt regressions.

Other control variables used by the aforementioned authors are the two-year average return on

equity (ROE2) and the relative sales growth over the last two years (S2). Both variables are

expected to have a negative influence on firm indebtedness. The size of the firms (SIZE) is expressed

as the log of total sales. Size and age are expected to have a positive influence on indebtedness.

Larger and more experienced firms face lower interest rates, making debt financing more attractive.

Their age (AGE) and the crisis intensity (CRIS) are measured as before. In times of financial distress

companies are expected to become more indebted. The industry and location effects are included by

the average debt ratios for each subindustry or each country in each year. The company

indebtedness is expected to be positively related with the subindustry and country averages.

Company-specific effects are included in the cross-sectional fixed effects.

27

These independent variables lead to the following estimation models for total debt, long-term debt

and short-term debt:

With INT = TASI†, TASI†ep, TNI† or TNI†

ep


Based on the work of Todo (2011) and others (Bernard&Jensen, 2004; Minetti&Zhu, 2011), size (SIZE)

will be expressed by the amount of employees working for the company and is expected to have a

positive influence on firm internationalization.

Financial constraints are taken into account through two measures: the average classic debt ratio

over the last two years (TDA2) will be used to represent the capital structure of the firms, while the

two-year average interest expense over debt ratio (IED2) will be used to assess the average interest

rate a company faces for paying of its debts. Both ratios are predicted to have a negative effect on an

AEC firm’s international behaviour.

The value added per employee is used to indicate the productivity of the enterprises. Here as well,

the average of the last two years is used (PROD2). Productivity is also expected to have a positive

effect on internationalization.

The international attitude of the company in the years before is measured by the change in

internationalization over the past two years (ΔINT2) and is anticipated to have a positive influence on

the international behaviour in the following year. To assess the influence of increased foreign sales in

the foregoing years on the internationalization decision, a dummy variable IFS2 is defined. It reaches

a value of 1 if the foreign sales have increased over the previous two years and a value of 0 in the

other case. Similarly a dummy variable DDS2 is defined which has a value of 1 if the domestic sales

have decreased over the two preceding years. Both IFS2 and the combination of IFS2 and DDS2 are

predicted to have a positive influence.


The average internationalization behaviour of firms in the same subindustry (INT_IND) and the same

country (INT_LOC) over the two previous years is expressed as the mean value of the relevant

internationalization measure. The AEC company’s internationalization behaviour in the coming year

is expected to be positively influenced by both averages.

Experience, education and operating resources are all different contributions to the quality of the

offered services. The education and experience of the individual employees relate to human capital

and can be gauged by the average wages and salaries per employee (WSE). The experience of the

firm and the size of the customer base the company gained over the years can be approximated by

its age (AGE). The intangible assets (ITA) of a company also provide a general insight into other

unspecified competitive advantages and intellectual capital. All are assumed to have a positive

influence on AEC firm internationalization.

The development of the financial crisis of 2008 (CRIS) is also added as a control variable and is

defined as before.

Company-specific effects which are not measured with the aforementioned variables, are included in

the cross-sectional fixed effects.

Finally, this results in the following regression equation:

With INT = TASI†, TASI†ep, TNI† or TNI†

ep 4

4 Since for each separate company, the TNI and TASI ratios only differ with the factor NSI, which is a company-specific

constant in this study, the percentage change of TNI and TASI are identical. This implies that for the regressions, only two

internationalization measures have to be tested: the European subsidiary-based TNI and the extrapolated TNI. The

corresponding TASI values give the same results.

29

5. PRIMARY RESULTS

Empirical subsidiary study

The companies included in the sample originate from thirteen different countries belonging to the

European Union in 2003 (figure 4). Only Austria and Luxembourg are not represented in the sample.

Given the smaller home markets for construction firms operating in a smaller nation as for example

Finland or Ireland, companies from these countries are represented more numerously in the sample

than one would expect based on their size. Expansion of enterprises in small countries will sooner or

later always involve crossing their borders (Spithoven&Teirlinck, 2005).

FIGURE 4. Domestic countries of the sample companies.

However, more than 40% of all mother firms come from the large, former colonial nations of Spain

and the United Kingdom. On one hand, linguistic barriers might be lower for these countries as

English and Spanish are by far globally the most spoken European languages. The larger economies of

these countries than the economies of for example Denmark or Greece also help explaining the more

numerous presence of Spanish and British companies. On the other hand, their colonial past might

facilitate the internationalization process for companies investing in subsidiaries in former colonial

Spain82

United Kingdom60

Italy42

Netherlands26

Finland25

Sweden23

Ireland17

Germany15

Denmark14

Belgium12

Portugal10

Greece9

France6

Spain

United Kingdom

Italy

Netherlands

Finland

Sweden

Ireland

Germany

Denmark

Belgium

Portugal

Greece

France

30 5. PRIMARY RESULTS

regions (Brewer, 2007; Ghemawat, 2001). Countries which declared their independence from the

European nations in the last centuries often still face a lower psychological distance with their former

colonisers. Both the cultural and the legal aspects of internationalization are more likely to be similar

between an ex-colony and their former European ruler.

German firms are represented less numerously in the sample. There might be multiple explanations

for this. The German language is mainly spoken in Germany and some neighbouring countries only.

Germany is also the most populous country and the largest economy of the EU. The limited use of

the German language on the global field as well as the large size of the German domestic market

might explain the relatively limited presence in the company sample.

Another large country with limited representation in the sample is France. As Spain and the United

Kingdom, France as well has a rich colonial history. French is also a global language, but to a smaller

extent than English or Spanish. Other unspecified causes for the limited representation of French AEC

companies might be related to the sample selection criteria.

85% of all mother companies in the sample are located in a European Union member state with the

euro as its currency in 2003. The other 15% originate from the United Kingdom, Sweden and

Denmark. This implies that most companies in the sample had to deal with the economic downturns

in the eurozone caused by the global financial crisis. The deterioration of the economic environment

was most pronounced in Greece, Portugal, Ireland, Spain and Cyprus. The sample includes firms from

all these nations (apart from Cyprus which was not yet member of the EU in 2003).

About 95% of the companies active in the European construction sector are micro or small

enterprises. However, the vast majority of these enterprises do not have the means to maintain

subsidiary companies. This is also reflected in the sample: no micro or small firms are included. Only

7% of the companies in the sample can be classified as medium sized. The majority of the companies

represented in the sample is however categorized as large (32%) or very large (60%). 5

5 The size classification is based on the definitions used by Bureau Van Dijk:

Very large: operating revenue ≥ 100 million EUR; total assets ≥ 200 million EUR; number of employees ≥ 1000

(excluding companies with ratios of operating revenue per employee or total assets per employee < 1000 EUR)

Large: operating revenue ≥ 10 million EUR; total assets ≥ 20 million EUR; number of employees ≥ 150

(excluding companies with ratios of operating revenue per employee or total assets per employee < 1000 EUR)

Medium-sized: operating revenue ≥ 1 million EUR; total assets ≥ 2 million EUR; number of employees ≥ 15

Small and micro: other companies

31

A majority of 60% of all companies in the sample is involved in building construction (Nace F.41). The

second largest (17%) subindustry represented in the sample is the construction of infrastructure,

water projects, utility projects and other civil engineering projects (Nace F.42). The smallest fractions

are occupied by the auxiliary activities of architects (7%; Nace M.71.1.1) and engineering

consultancies (16%; Nace M.71.1.2).

The companies involved with the actual construction of construction projects (Nace codes F.41 and

F.42) have a similar size distribution. The majority of them are classified as very large (61% and 75%

respectively). Only a minor fraction of these enterprises are classified as medium-sized (5% and 3%

respectively). This asymmetric size distribution can also be observed in the auxiliary subindustries,

although less pronounced. The architectural sector (M.71.1.1) is the most evenly distributed with

26% medium-sized firms, 35% large companies and 39% very large enterprises included in the

sample. The exact numbers can be found in annex 1.

As could be expected, larger firms have control over a larger group of subsidiaries. Figure 5 shows

the cumulative amount of parent companies for different amounts of subsidiaries. Since the sample

does not include micro or small enterprises, no information regarding them can be verified from this

sample. Medium-sized companies own on average three subsidiaries. Large firms control about ten

subsidiaries and the very large enterprises in the sample even own fifty subsidiaries on average.

Taking the different subindustries into account (figure 6) it is clear that the actual construction

companies in the sample have a larger pool of subsidiaries they can outsource activities to.

Engineering consultancies engage less in investing in subsidiaries and this is even more so for

architectural companies. The operational activities from the latter two sub-sectors can be carried out

from all over the world. Rules and regulations concerning the design of constructions show a lot of

parallels across different countries, with now and then other formulas or safety coefficients. This

makes it possible for these firms to work for foreign clients in their home office. The work of

companies engaging in the execution of the construction projects on the contrary requires a

presence on the local construction site. Having subsidiaries in regions further away from the parent

company’s home region then allows for a larger client market.


FIGURE 5. Cumulative percentage of parent companies in function of the amount of subsidiaries they control for the different size categories.

FIGURE 6. Cumulative percentage of parent companies in function of the amount of subsidiaries they control for the different subindustries.

0,00%

10,00%

20,00%

30,00%

40,00%

50,00%

60,00%

70,00%

80,00%

90,00%

100,00%

1 10 100 1000

Cu

mu

lati

ve f

req

ue

ncy

(% o

f to

tal

amo

un

t in

eac

h s

ize

cat

ego

ry)

Amount of subsidiaries

Medium sized Large Very large

0,00%

10,00%

20,00%

30,00%

40,00%

50,00%

60,00%

70,00%

80,00%

90,00%

100,00%

1 10 100 1000

Cu

mu

lati

ve f

req

ue

ncy

(%

of

tota

l am

ou

nt

in e

ach

siz

e c

ate

gory

)

Amount of subsidiaries

NACE 41 NACE 42 NACE 7111 NACE 7112

33

The barrier to foreign investment is the lowest for subsidiaries in neighbouring countries (figure 7).

About six in ten companies controlling foreign subsidiaries has at least one subsidiary in a

neighbouring country. Host countries with the mother language of the internationalising company as

an official (de facto) language also attract more internationally expanding enterprises. 46% of the

parent companies with foreign subsidiaries control a subsidiary in a host country speaking their

native language. Historical ties also prove their use for subsidiary location selection. Countries which

were colonies, protectorates or overlords of the European home countries of the parent firms

convinced 47% to invest in a subsidiary in these host countries.

The intensity of subsidiary investment in countries with a high geographical, linguistic or historical

proximity is lower than in the domestic country (figure 8). These proximate foreign countries

convince companies to invest in on average four to six subsidiaries.

FIGURE 7. Percentage of mother companies investing in at least one subsidiary in each of the different categories. 6

91

,79

%

58

,06

%

46

,04

%

46

,92

%

97

,07

%

17

,30

%

16

,13

%

0,00%

10,00%

20,00%

30,00%

40,00%

50,00%

60,00%

70,00%

80,00%

90,00%

100,00%

Do

me

stic

co

un

try*

Ne

igh

bo

uri

ng

cou

ntr

ies

Sam

e la

ngu

age

His

tori

cal t

ies

TRIA

D c

ou

ntr

ies*

*

BR

IC c

ou

ntr

ies*

**

Eme

rgin

g co

un

trie

s**

**

Pe

rce

nta

ge o

f in

vest

ing

mo

the

r co

mp

anie

s

Different categories of subsidiary countries

6 * Domestic country, including territories and dependencies

** Northern America (Canada, U.S.A.), Western Europe (EU 2003, Switzerland, Norway, Iceland and the European

micro-nations) and Japan

*** Brazil, Russia, India and China

**** according to Next 11 (Sachs, 2007), CIVETS (Allen, 2011), MINT (Wright, 2014), VISTA (Wang, 2008), MIKTA (Cooper,

2015)


FIGURE 8. Average amount of subsidiaries of firms investing in subsidiaries in each of the different categories. 6

Regarding the economic development of the host country, three categories are defined. The first one

contains the triad countries of Northern America, Western Europe and Japan. The second group

comprises the four new economic powers of Brazil, Russia, India and China. The last category

contains 15 emerging economies included in the Next 11 (Sachs, 2007), CIVETS (Allen, 2011), MINT

(Wright, 2014), VISTA (Wang, 2008) and MIKTA (Cooper, 2015) definitions frequently used by

researchers. 7

Almost all companies invest in a subsidiary in a triad region. 92% of the firms control a subsidiary in

their home country. Another 5% of the companies invest in a subsidiary in Canada, the United States

of America or Japan. These results support the idea of Rugman (2005) and Rugman and Collinson

(2006) that the triad regions account for the largest portion of intraregional trade, while interregional

trade is still the most important. Furthermore, respectively 16% and 17% of the enterprises is

attracted to invest in a BRIC or other emerging country. The subsidiary investment in these regions is

on average again limited to four to six subsidiaries.

25

46 6

28

46

0,00

5,00

10,00

15,00

20,00

25,00

30,00

Do

me

stic

co

un

try*

Ne

igh

bo

uri

ng

cou

ntr

ies

Sam

e la

ngu

age

His

tori

cal t

ies

TRIA

D c

ou

ntr

ies*

*

BR

IC c

ou

ntr

ies*

**

Eme

rgin

g co

un

trie

s**

**

Ave

rage

am

ou

nt

of

sub

sid

iari

es

Different categories of subsidiary countries

7 The countries included in this selection of emerging economies are listed in annex 2.

35

Finally, to look at how the subsidiaries of the Western European parent companies are spread

around the world, all subsidiaries were localized in one of the UN subregions as defined by the

United Nations Statistical Division (UNSD, 2013) in their M.49 coding classification (figure 9). 1% of

the subsidiaries’ home country was not defined. The definition of these subregions and the

corresponding distribution percentages can be found in annex 3.

A large majority of the subsidiaries in the sample (72%) are located in the domestic country of its

parent company. Another 15% are located in fellow European Union (2003) states, new EU members

and other European countries. The remaining 13% is located outside Europe. More than half of these

subsidiaries are based on the American continent and Africa and Asia each accommodate about a

sixth of them.

These numbers might seem contradictory with the numbers before, but one should make a clear

distinction between the percentage of subsidiaries in a region and the percentage of mother

companies investing in these regions. Not all regions contain the same concentration of subsidiaries.

Looking at the geographical interests of the parent companies (figure 10), it becomes clear that the

other European countries, Northern and Southern America and the region stretching from Northern

Africa to Western Asia can still charm a sizeable amount of mother companies. Their reflection in the

subsidiary spread is however less pronounced since companies investing in these regions have on

average 25 subsidiaries in their home country, whereas the other regions can only count on 2 to 6

subsidiaries (figure 11). The subsidiary investment in these regions is thus much scarcer than in the

domestic country.


FIGURE 9. Geographical distribution of subsidiaries.

FIGURE 10. Percentage of mother companies investing in at least one subsidiary in each of the different UN subregions.

Main mother country

Other EU-2003

New EU 2003-2013

Other Eur.

N. AmericaCentr. America

Caribbean

S. America

N. Africa

W. Africa

Mid. AfricaE. Africa

S. Africa

W. Asia

Centr. Asia

S. Asia

SE. Asia

E. Asia

AU & NZ

Pacific

Unknown country

91

,79

%

55

,72

%

39

,88

%

19

,35

%

17

,89

%

9,3

8%

3,5

2% 1

4,6

6%

10

,56

%

5,2

8%

4,4

0%

3,2

3%

2,9

3% 11

,14

%

1,4

7%

5,5

7%

6,7

4%

6,1

6%

4,9

9%

0,0

0%

0,00%

10,00%

20,00%

30,00%

40,00%

50,00%

60,00%

70,00%

80,00%

90,00%

100,00%

Mai

n m

oth

er

cou

ntr

y

Oth

er

fello

w E

U 2

00

3 c

ou

ntr

ies

Ne

w E

U c

ou

ntr

ies

20

03

-20

13

Oth

er

Euro

pe

No

rth

ern

Am

eri

ca

Ce

ntr

al A

me

rica

Car

ibb

ean

Sou

th A

me

rica

No

rth

ern

Afr

ica

We

ste

rn A

fric

a

Mid

dle

Afr

ica

East

ern

Afr

ica

Sou

the

rn A

fric

a

We

ste

rn A

sia

Ce

ntr

al A

sia

Sou

the

rn A

sia

Sou

the

aste

rn A

sia

East

ern

Asi

a

Au

stra

lia &

Ne

w Z

eal

and

Pac

ific

Pe

rce

nta

ge o

f in

vest

ing

mo

the

r co

mp

anie

s

Different UN subregions

37

FIGURE 11. Average amount of subsidiaries of firms investing

in subsidiaries in each of the different UN subregions.

25

54 3

56

1

6

2 24 3 3

2 2 23 2

3

00,00

5,00

10,00

15,00

20,00

25,00

30,00

Mai

n m

oth

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cou

ntr

y

Oth

er

fello

w E

U 2

00

3 c

ou

ntr

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Ne

w E

U c

ou

ntr

ies

20

03

-20

13

Oth

er

Euro

pe

No

rth

ern

Am

eri

ca

Ce

ntr

al A

me

rica

Car

ibb

ean

Sou

th A

me

rica

No

rth

ern

Afr

ica

We

ste

rn A

fric

a

Mid

dle

Afr

ica

East

ern

Afr

ica

Sou

the

rn A

fric

a

We

ste

rn A

sia

Ce

ntr

al A

sia

Sou

the

rn A

sia

Sou

the

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rn A

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Asi

a

Au

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lia &

Ne

w Z

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and

Pac

ific

Ave

rage

am

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nt

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es

Different UN subregions


Comparison of export data and subsidiary data

Since only a few companies have export data available in the Amadeus database, a subsidiary

method was developed. In this chapter, the accurateness of this approximation is tested by

regressing the limited available export data (FSTS or export percentage) with corresponding

subsidiary-based internationalization measures. The independent variables used as input for the

different regressions are the TNI and TASI indices both for the European subsidiaries only and for all

subsidiaries. Both the TNI and TASI ratios are defined as the average of the sales, assets and

employees aspects. For this reason all these constituents are regressed separately as well:

- TNI†

- TNI†ep

- TASI†

- TASI†ep

- FXTX† series: FSTS†, FATA† and FETE†

- FXTX†ep series: FSTS†

ep, FATA†ep and FETE†

ep

- FXTX† x NSI† series: FSTS† x NSI†, FATA† x NSI† and FETE† x NSI†

- FXTX†ep x NSI† series: FSTS†

ep x NSI†, FATA†ep x NSI† and FETE†

ep x NSI†

The complete results of these regressions can be found in annex 4. A summary can be found in tables

1 and 2 below. All variables are winsorized at the 1% and 99% quantiles and standardized. In all

regressions, cross-sectional fixed effects were implemented as well as White cross-sectional robust

standard errors to avoid multicollinearity and heteroskedasticity problems. Autocorrelation is

checked by looking at the Durbin-Watson statistics. For all regressions this statistic has a value close

to 2, implying that autocorrelation is not a problem. Endogeneity is controlled manually by verifying

that all covariances of the independent variables with the residuals are close to zero. Endogeneity is

not a problem for these regressions.

Overall, the results show a good fit between the actual export percentage and the different

subsidiary-based measures. The subsidiary-based FSTS ratios show the best results, while the assets

and employees ratios perform best when scaled with the NSI factor. The actual export percentage is

approximated best by the extrapolated subsidiary sales ratio, corrected with the corresponding

assets ratio and employees ratio. Furthermore, the TASI measure also performs better than the TNI

measure which does not take the geographical scope into account, suggesting that this geographical

spread has a non-negligible influence when approximating the real export percentage with subsidiary

data.

39

REGRESSION

WITH TNI†

REGRESSION

WITH TNI†EP

REGRESSION

WITH TASI†

REGRESSION

WITH TASI†EP

Coefficient 2.9693 0.4429 2.5221 5.2594

t-statistic 1.6010 2.8546 3.1747 3.1395

Prob(t-stat) 0.1132 0.0054 *** 0.0021 *** 0.0023 ***

R² 0.7248 0.7332 0.7488 0.7387

R²adj 0.6353 0.6464 0.6671 0.6537

Σμ²i,t 45.3239 43.9499 41.3805 43.0369

F-statistic 8.0978 8.4471 9.1625 8.6915

Durbin-Watson statistic 2.5541 3.1379 2.9080 3.0992

Total panel observations 111 111 111 111

TABLE 1. Summary of the regression results for the TNI and TASI indices.

(***, ** and * represent significance levels of respectively 1%, 5% and 10%.)

REGRESSION

FXTX† SERIES

REGRESSION

FXTX†EP SERIES

REGRESSION

FXTX† X NSI†

SERIES

REGRESSION

FXTX†EP X NSI†

SERIES

Sale

s

Coefficient 0.5437 1.5916 5.4022 18.2869

t-statistic 2.2185 2.0635 5.4188 8.1188

Prob(t-stat) 0.0293 ** 0.0423 ** 0.0000 *** 0.0000***

Ass

ets

Coefficient -0.0061 -0.4341 -3.3607 -4.1387

t-statistic -0.0311 -0.4992 -1.9499 -1.4949

Prob(t-stat) 0.9753 0.6190 0.0547 * 0.1388

Emp

loye

es Coefficient 1.1780 1.5908 6.2795 1.5871

t-statistic 0.4625 0.6848 4.246074 3.7731

Prob(t-stat) 0.6449 0.4954 0.0001 *** 0.0003 ***

R² 0.7671 0.7575 0.8376 0.8545

R²adj 0.6837 0.6706 0.7794 0.8025

Σμ²i,t 38.3680 39.9495 26.7555 23.9588

F-statistic 9.1980 8.7233 14.4023 16.4096

Durbin-Watson stat 2.6451 2.7424 2.4200 2.9651


TABLE 2. Summary of the regression results for the separate "sales", "assets" and "employees" terms.



One should however take in mind that only the real FSTS is regressed on, since this was the only

export number which was available. However, the results above suggest that the subsidiary-based

method may provide good approximations for the other internationalization measures as well.

In the following chapters, the relations between firm internationalization and the profitability and

indebtedness of AEC companies are examined, as well as the drivers of internationalization. The

internationalization measures used in these chapters are all subsidiary-based. Each time, both the

values based on the European subsidiaries and the extrapolated values are tested.

41

Profitability

Descriptive statistics

The descriptive statistics of the non-standardized parameters used to examine the relation between

internationalization and profitability can be found in annex 5. The descriptive values of the ROA

show that the sample contains both firms with high ROA’s (32%) and firms with low ROA’s due to

losses (-26%). The average firm however has a ROA of about 5%.

Furthermore the lion share of the sampled companies for this regression has rather low

internationalization ratios. For this reason, it might be that the downward slope (phase 3) in figure 3

will not be visible for the used sample data.

The highest correlations exist between the different tested internationalization measures. These

values are not implemented in the regression at the same time though, so this should not give any

specific trouble with the regression.

Regressions

Eight regressions were tested to examine the effects of internationalization on the profitability of

European AEC enterprises. To this extent four different measures are included, each once based on

the data from the European subsidiaries and once extrapolated to the whole subsidiary base: the

FSTS ratio, the FSTS x NSI ratio, the TNI ratio and the TASI ratio. The values of all observations are

winsorized at the 1% and 99% quantiles and standardized. All regressions are executed with cross-

sectional fixed effects and White cross-sectional robust standard errors to avoid multicollinearity and

heteroskedasticity problems. The Durbin-Watson statistics all have a value close to 2, indicating that

autocorrelation is not a problem. By checking that all covariances of the independent variables with

the residuals are sufficiently small, it was verified that endogeneity is not problematic for the

regression series at hand.

The different regressions results are included in annex 6. A summary of these results can be found in

table 3 and table 4 below.


REGRESSION

WITH FSTS†

REGRESSION

WITH FSTS†EP

REGRESSION

FSTS† X NSI†

REGRESSION

FSTS† EP X NSI†

Lin

ear

term

Coefficient -0.6575 -0.2305 -0.0508 -0.8378

t-statistic -4.6112 -1.6245 -0.2604 -1.7997

Prob(t-stat) 0.0000 *** 0.1049 0.7947 0.0725 *

Qu

adra

tic

term

Coefficient 1.8869 0.1092 -0.8107 4.031

t-statistic 3.3448 0.2264 -0.8331 1.4457

Prob(t-stat) 0.0009 *** 0.8210 0.4052 0.1489

Cu

bic

ter

m Coefficient -1.4383 -0.0827 0.8080 -8.5308

t-statistic -2.8393 -0.2640 1.0143 -1.3971

Prob(t-stat) 0.0047 *** 0.7919 0.3110 0.1630

R² 0.6658 0.7167 0.7137 0.7112

R²adj 0.5783 0.6250 0.6211 0.6177

Σμ²i,t 279.4227 159.7135 161.3798 162.7966

F-statistic 7.6100 7.8157 7.7031 7.6092



TABLE 3. Summary of the regression results for the profitability: FSTS-based measures.


The FSTS† shows the best results and confirms the shape of figure 3. The other FSTS-based and

TNI/TASI-based internationalization ratios do not lead to the same level of significance, but the

coefficient signs suggest the same shape. For increasing internationalization levels the profitability at

first decreases. After reaching a minimum, the companies become more profitable again. This

increased profitability then nears a maximum after which another downward slope begins. This

conclusion is completely in accordance with figure 3 and the findings of Contractor et al. (2003),

Geringer et al. (1989), Lu and Beamish (2004), Riahi-Belkaoui (1998) and Ruigrok et al. (2007). A

remark with this assessment is that most observations are situated in phases 1 and 2 of figure 3, with

the largest part of the observations in the declining part of phase 1. However, this does not detract

from the conclusion above.

The quadratic shape was not significant in any of the tested regressions, while the linear shape is

found twice, but with much lower significance levels than the FSTS† regression. This linear relation

however supports the findings of Horst (1972), Shaked (1986), Siddharthan and Lall (1982) and

43

Ruigrok et al. (2007). This negative linear relation is probably also due to the majority of the

observations lying in the downward part of phase 1.

REGRESSION

WITH TNI†

REGRESSION

WITH TNI†EP

REGRESSION

WITH TASI†

REGRESSION WITH

TASI†EP

Lin

ear

term

Coefficient -3.0741 -0.1370 -0.2220 0.0730

t-statistic -1.7219 -0.6840 -0.9413 0.1326

Prob(t-stat) 0.0857 * 0.4943 0.3470 0.8946

Qu

adra

tic

term

Coefficient 60.3191 0.0729 0.3918 -2.2282

t-statistic 1.1014 0.1237 0.9209 -1.1759

Prob(t-stat) 0.2713 0.9016 0.3576 0.2402

Cu

bic

ter

m Coefficient -345.9829 -0.0100 -0.2106 1.4576

t-statistic -0.7701 -0.0209 -0.7765 1.2810

Prob(t-stat) 0.4416 0.9833 0.4378 0.2008

R² 0.6898 0.7104 0.7090 0.7088

R²adj 0.5923 0.6167 0.6148 0.6146

Σμ²i,t 188.4667 163.2436 164.0449 164.1412

F-statistic 7.0690 7.5799 7.5278 7.5216



TABLE 4. Summary of the regression results for the profitability: TNI- and TASI-based measures.


Considering the control variables, the company size (measured in assets), the firm age and the

subindustry mean ROA proved to have a very significant influence as well: size and age reached

significance levels of 1% and the industry-average profitability obtained levels of significance of 1% to

5%. The firm profitability was positively related with age and industry-average profitability and

negatively related with firm size. This can be explained by reduced company efficiency when the firm

grows (expressed in assets). On the other hand, older firms already have more experience in their

activity domain, which is translated in higher profits. The positive relation with the subindustry’s

average profitability indicates that companies in the AEC sectors depend on the state of their

subindustry in general to a large extent. The effect of the global financial crisis is partly included in

this industry-average. On the other hand, the CRIS variable measures the country’s global GDP

growth, also considering the other industries. The low significance of this CRIS variable in all


regressions, combined with the high significance of the industry-average profitability show that the

profits of AEC constructions depend on the state of the different AEC subindustries, but not on the

state of the other industries.

45

Indebtedness


The descriptive statistics of the non-standardized parameters used to assess the different

indebtedness relations can be found in annex 7. The descriptive values of the TDA, LDA and SDA

ratios show that in general European AEC companies finance the lion share of their assets with debt.

This debt is composed of roughly one third long-term debt and two thirds short-term debt. On one

hand this might be due to the uncertain economic outlook during the crisis years, discouraging

companies to take on long-term liabilities. On the other hand, contractor companies work with fairly

large amounts of supplier credit, raising the value of their short-term debts.

The descriptive statistics of the TNI and TASI coefficients, both for the European subsidiaries and

extrapolated for all subsidiaries, show that the sampled enterprises have rather low levels of

internationalization with only a few companies reaching higher values. Together with the high

percentages of short-term debt, this supports the findings of Singh and Nejadmalayeri (2004) that

less internationalized companies have more trouble finding long-term financial resources and resort

more often to short-term debt.

The highest correlations occur between the different TNI and TASI coefficients which is logical since

these all depart from the same data. However, since these are never tested simultaneously this

should not cause any trouble with the regressions.

Regressions

To measure the influence of internationalization on the indebtedness of companies, twelve

regressions were tested: three debt aspects (total debt, long term debt and short term debt) and

four internationalization measures (TASI†, TASI†ep, TNI† and TNI†

ep) are combined. The values of all

observations are winsorized at the 1% and 99% quantiles and standardized. Cross-sectional fixed

effects and White cross-sectional robust standard errors are adopted to avoid coefficient biases and

problems related to multicollinearity and heteroskedasticity. Autocorrelation is checked by looking at

the Durbin-Watson statistics, which for all regressions has a value close to 2, implying that

autocorrelation is not problematic. Endogeneity is controlled manually by verifying that all

covariances of the independent variables with the residuals are close to zero. After this check

endogeneity proved not to be problematic.

The different regressions results are included in annex 8. A summary of these results can be found in

tables 5 to 7 below.


TOTAL DEBT REGRESSION

WITH TASI†

REGRESSION

WITH TASI†EP

REGRESSION

WITH TNI†

REGRESSION

WITH TNI†EP

Lin

ear

term

Coefficient -0.1218 -0.1018 -0.1217 -0.1129

t-statistic -1.9674 -1.5020 -1.5966 -1.7553

Prob(t-stat) 0.0503 * 0.1344 0.1117 0.0805 *

R² 0.9060 0.9036 0.9057 0.9059

R²adj 0.8660 0.8626 0.8656 0.8659

Σμ²i,t 16.2753 16.6901 16.3188 16.2843

F-statistic 22.6419 22.0207 22.5753 22.628



TABLE 5. Summary of the regression results for the total debt ratio TDA.


All four internationalization measures give similar results. Both the total debt and the long-term debt

ratios are negatively related to the internationalization level. These results conflict with the findings

of Singh and Nejadmalayeri (2004). However Chen, Cheng, He and Kim (1997) also found a negative

relation between internationalization and debt, with domestic companies carrying higher debts than

their more internationalized colleagues. The lower debt ratios for higher levels of internationalization

imply that they finance their activities more with short-term debt and especially more with equity.

This equity consists both of equity invested by shareholders and accumulated company profits.

Lastly, one should also note that even though the results are statistically significant, the economical

significance is rather limited.

For the short-term debt ratio a quadratic relation with internationalization proved adequate, as Singh

and Nejadmalayeri (2004) found. For the observations used in the regression, no maximum is

reached though. Instead, a minimum can be found at higher internationalization levels. This

observation might however be specific for the AEC sector. Less internationalized AEC companies

already have to bridge a significant need for working capital and supplier credit. Furthermore, this

short-term debt reliance might be correlated with the perception of investors. Companies with low

levels of internationalization might be perceived as less experienced. Also, less internationalized

firms who engage in small amounts of foreign trade might face higher interest costs when issuing

long-term liabilities, forcing them to resort to short-term debts and equity (Titman&Wessels, 1988).

More internationalized firms have access to a larger base of possible credit providers, allowing them

47

to decrease their dependency on short-term debt. Yet highly internationalized firms might as well be

viewed as too dependent on the risky undertaking foreign business can be.

LONG-TERM DEBT REGRESSION

WITH TASI†

REGRESSION

WITH TASI†EP

REGRESSION

WITH TNI†

REGRESSION

WITH TNI†EP

Lin

ear

term

Coefficient -0.0805 -0.0586 -0.0443 -0.0295

t-statistic -2.2990 -2.6769 -2.0953 -1.5845

Prob(t-stat) 0.0224 ** 0.0079 *** 0.0372 ** 0.1144

R² 0.8433 0.8424 0.8422 0.8420

R²adj 0.7766 0.7753 0.7750 0.7747

Σμ²i,t 42.2904 42.5351 42.5890 42.6455

F-statistic 12.6452 12.5590 12.5401 12.5204



TABLE 6. Summary of the regression results for the long-term debt ratio LDA.


SHORT-TERM DEBT REGRESSION

WITH TASI†

REGRESSION

WITH TASI†EP

REGRESSION

WITH TNI†

REGRESSION

WITH TNI†EP

Lin

ear

term

Coefficient -0.3221 -0.1671 -0.9204 -0.3585

t-statistic -1.9559 -0.3752 -2.6710 -1.9809

Prob(t-stat) 0.0516 * 0.7078 0.0081 *** 0.0487 **

Qu

adra

tic

term

Coefficient 0.2288 0.1229 0.8597 0.2489

t-statistic 1.6658 0.2864 2.5186 1.6813

Prob(t-stat) 0.0970 * 0.7748 0.0124 ** 0.0940 *

R² 0.8339 0.8324 0.8381 0.8351

R²adj 0.7622 0.7601 0.7682 0.7640

Σμ²i,t 30.2355 30.5077 29.4755 30.0077

F-statistic 11.6328 11.5084 11.9925 11.7388

Durbin-Watson stat 2.3924 2.3528 2.407358 2.4113


TABLE 7. Summary of the regression results for the short-term debt ratio SDA.



Both the two-year average ROE and the sales growth over the last two years have a negative

influence on long-term indebtedness. This can be attributed to the higher capital reserves the firms

have developed along with their success. These reserves enable companies to rely less on long-term

debt financing. ROE however proved to exert a positive influence on short-term indebtedness. A

possible explanation might be that the higher return requires a higher amount of supplier credit,

which in turn results in a higher short-term indebtedness.

Regarding size, it is found that larger companies carry more long-term and overall debt. This is in

accordance with the expectations.

Age on the contrary has a negative effect on short-term and overall debt. This might be explained by

the company’s experience. Older companies are in general more experienced and have accumulated

more financial reserves to buffer financial setbacks, hence they do not need to resort to short-term

debts that often.

The overall effect of the global financial crisis on total company debt is not clearly visible. Yet in years

with lower GDP growth enterprises rearrange their capital structure, converting short-term debt in

long-term debt. This suggests that AEC companies respond well to the financial markets.

The highly significant positive relation between company indebtedness and the country-average

company indebtednesses confirms the expectation that the capital structures of AEC firms in the

same country follow the same pattern. The long-term indebtedness is furthermore also positively

influenced by industry-average indebtedness across country borders, indicating industry-specific

effects.

49



The descriptive statistics of the non-standardized parameters used to assess the different

indebtedness relations can be found in annex 9.

Regressions

The drivers of internationalization are assessed by regressing the various independent variables

described in chapter 4 on the percentage change in internationalization in the upcoming year. The

relative changes in TNI and TASI are identical since the NSI’s of the companies are assumed fixed in

this study. For this reason, only the relative change in TNI is studied. Both the TNI’s based on the

European subsidiaries and the extrapolated TNI’s are examined. The values of all observations are

first winsorized at the 1% and 99% quantiles and then standardized. All regressions are executed with

cross-sectional fixed effects and White cross-sectional robust standard errors to avoid

multicollinearity and heteroskedasticity problems. Autocorrelation is checked by looking at the

Durbin-Watson statistics. For both regressions autocorrelation was a problem. This problem was

solved by correcting all regression variables for autocorrelation. After this correction, this statistic

has a value close to 2 for all regressions, implying that autocorrelation is no longer a problem.

Control for endogeneity by manual verification that all covariances of the independent variables with

the residuals are close to zero shows that for these regressions no endogeneity-related problems

arise.

The different regressions results are included in annex 10. A summary of these results can be found

in tables 8 to 10 below.

Both regressions show a positive influence of size, indicating that larger firms focus more on

internationalization. This finding is in accordance with the studies of Cavusgil (1984), Mayer and

Ottaviano (2007), Ogbuehi and Longfellow (1994), Todo (2011) and Tookey (1964). Larger AEC

companies have fewer problems to overcome additional internationalization costs, while they have

in general more resources to resort to in case the internationalization turns awry.

No conclusive effects are found regarding the financial situation of AEC firms.

The average productivity over the last two years as well as the GDP growth proved to have a negative

influence, suggesting that AEC companies decrease their reliance on international business in times

of high productivity and vice versa. This observation is opposed to the findings of Bernard and Jensen

(1999), Helpman et al. (2004), Melitz (2003), Miyakawa et al. (2013) and Todo (2011) but it might


indicate that internationalization is perceived as a counterbalance for unsatisfactory productivity

levels.

Somewhat unexpectedly, an increased internationalization in the previous years is not continued into

the next year. On the contrary: an increased internationalization seems to be followed by a

decreased internationalization and vice versa. This might suggest that the process of

internationalization of AEC firms is characterised by a lot of trial and error and that the risks of

relying more on foreign subsidiaries are not always gauged correctly.

The internationalization behaviour of AEC companies in the same subindustry over the past two

years shows a significant negative influence, which can indicate that the trial and error process of

internationalization mentioned above occurs in waves across the subindustries.

COEFFICIENT T-STATISTIC PROB(T-STAT)

Constant 0.1452 0.2258 0.8219

Size 0.6533 3.1530 0.0022 ***

TDA2 -0.0381 -0.2862 0.7754

IED2 -0.0834 -1.1250 0.2639

PROD2 -0.1694 -3.3457 0.0012 ***

ΔTNI2 -0.3061 -3.8494 0.0002 ***

IFS2 -0.0310 -0.3933 0.6951

DDS2 x IFS2 0.0691 0.9998 0.3203

ΔTNI2_IND -0.0722 -2.0885 0.0398 **

ΔTNI2_LOC 0.0206 0.8862 0.3781

WSE -0.0356 -0.3864 0.7002

AGE -0.9042 -2.9064 0.0047 ***

ITA 0.0760 0.5749 0.5669

CRIS -0.0764 -4.1279 0.0001 ***

TABLE 8. Summary of the regression results for the drivers: regression coefficients for the European subsidiaries.


51

COEFFICIENT T-STATISTIC PROB(T-STAT)

Constant -1.8812 -3.4921 0.0008 ***

Size 0.3657 2.5556 0.0124 **

TDA2 0.0546 0.4838 0.6298

IED2 -0.0830 -1.3466 0.1818

PROD2 -0.3146 -10.6208 0.0000 ***

ΔTNI2ep -0.4516 -7.2227 0.0000 ***

IFS2ep -0.0190 -0.2804 0.7799

DDS2ep x IFS2ep 0.1455 3.4982 0.0008 ***

ΔTNI2ep_IND 0.0137 0.3584 0.7210

ΔTNI2ep_LOC -0.0037 -0.1704 0.8651

WSE -0.0966 -1.3766 0.1724

AGE 0.6861 2.5848 0.0115 **

ITA 0.0036 0.0816 0.9351

CRIS -0.0069 -0.2389 0.8118

TABLE 9. Summary of the regression results for the drivers: regression coefficients for the extrapolated measures.


ΔTNI ΔTNI ep

R² 0.9120 0.9132

R²adj 0.8473 0.8494

Σμ²i,t 10.9188 7.1110

F-statistic 14.0970 14.3143

Durbin-Watson statistic 2.6342 2.3419

Total panel observations 145 145

TABLE 10. Summary of the regression results for the drivers: regression parameters.

The second regression also shows that if over the course of the past two years both the domestic

sales decreased and the foreign sales increased, the AEC company increases its level of

internationalization. This finding is completely in line with the work of Cavusgil (1984).

The employee wages and salaries and the AEC company’s intangible assets show no influence on the

internationalization behaviour of the firm.


Age has a contrary effect on the internationalization of a company, depending on the measure. The

percentage change of sales, assets and employees of the European subsidiaries seems to decrease

over time, while the change in weight of the total amount of subsidiaries increases with age. This

might suggest that younger AEC companies focus more on the European construction market and

start expanding outside Europe at older ages. Given the age distribution of the sampled companies,

this might also explain why the majority of the subsidiaries in the empirical study are located in

Europe.

Lastly, for the second regression the company-specific fixed effects appeared to have a certain

influence, indicating that besides the studied influences in this dissertation, still other unspecified

company-specific influences exist.

The deviation of the results from some expectations might be due to the used internationalization

measures. Todo (2011) and many other authors use dummy variables classifying firms according to

being purely domestic, exporting or investing in subsidiaries without considering the actual scale of

these export and investment decisions. Finally, one should realize as well that many of the stated

hypotheses arise from literature on qualitative assessment of internationalization drivers.

53

6. CONCLUSION, DISCUSSION, AND LIMITATIONS

This study examined internationalization in the European AEC industry. Both the effects on firm

profitability and on company indebtedness were assessed as well as what drives enterprises to focus

more on internationalization. All these topics were analyzed using regressions. Besides these

regressions, an empirical study was executed as well on the geographical spread of companies’

foreign activities.

Given the limited availability of export data in the Amadeus database, subsidiary data were used as

approximation instead. An additional assumption regarding this approximation is that each

subsidiary or parent company is only active in its own domestic country. This is of course a very

strong assumption, since not all exporting firms invest in foreign subsidiaries and vice versa. Since

Amadeus only provides data on European companies, the required subsidiary data were unavailable

for subsidiaries from other continents. In an attempt to tackle this limitation, the data from the

European subsidiaries were also extrapolated to the total subsidiary population of a parent firm.

Since neither the original data, nor the extrapolated data proved remarkably better in explaining the

drivers and effects of internationalization, both methods were retained. The subsidiary-based

approximation relies on strong assumptions nonetheless. For this reason, the quality of this

approximation was tested by regressing the limited available export data on the corresponding

subsidiary data. The comparison of the limited actual export data and different subsidiary-based

internationalization measures showed however that the export data could be related reasonably well

to subsidiary-based measures despite the strong assumptions.

Regarding the profitability of internationalizing AEC firms, the findings concur with the existing

literature in that three phases can be perceived. At low levels of internationalization the companies

face significant costs but low revenues, resulting in a low profitability. In a next stage they gain more

experience on international business. The revenues start increasing more than the costs, leading to

higher profits. However, when enterprises rely too much on internationalization, the coordination

costs overtake the revenues and the profitability drops.

The findings of the indebtedness regressions show that for the sampled AEC companies higher levels

of internationalization are associated with lower total and long-term debt ratios. For short-term debt

similar results are obtained. However for higher degrees of internationalization the share of short-

term liabilities increases again. This effect might relate to the risk perception of financial creditors.

Companies with low levels of internationalization might be perceived as less experienced, whereas

54 6. CONCLUSION, DISCUSSION, AND LIMITATIONS

highly internationalized firms might be viewed as too dependent on the risky undertaking foreign

business can be. Lastly, one should still keep in mind that these findings were obtained for the

subsidiary-based internationalization measures used for this study. Direct internationalization

measures as well as other samples might show different results.

With the final regression series, this study aims to quantitatively predict the drivers of

internationalization. Size is found to exert a positive influence on AEC company internationalization.

Larger firms are more likely to increase their foreign interests. Another positive effect is due to

increased foreign sales in combination with decreasing domestic sales. The success of foreign

business in combination with misfortune in the domestic market might persuade companies to focus

to a greater extent on the more promising international business. Furthermore the results suggest

that internationalization (measured as the share of foreign subsidiaries in the total company sales,

assets and employees) evolves in waves with periods of increased interest in internationalization and

periods of lower interest. Firm age proved to have an influence on the spread of subsidiaries.

Younger European AEC companies focus more on the European market, but when enterprises grow

older and gather more experience, they increase their scope to markets outside Europe. Lastly,

unspecified firm-specific effects seemed to have an influence as well. Contrary to existing literature

productivity appeared to have a negative influence on the internationalization of AEC enterprises. A

possible explanation might be that internationalization is perceived as counterbalance to

compensate for unsatisfactory productivity levels, but this is but a tentative explanation. Further

research in this domain is still necessary, not only to verify some unexpected results of this study, but

also since not much research has been executed in this domain. Furthermore most research on this

subject did not consider actual values of internationalization. Most researchers so far performed

qualitative studies (e.g. Cavusgil, 1984; Ogbuehi&Longfellow, 1994; Tookey, 1964) or have

implemented dummies to distinguish domestic companies from exporting firms and from enterprises

investing in foreign subsidiaries (e.g. Todo, 2011). A last remark on this topic is that this study used

subsidiary-based data. Actual export data might result in other findings.

Besides the regressions an empirical study on the distribution of the subsidiaries was carried out as

well. The results indicate a positive relation between parent company size and the amount of

subsidiaries. Furthermore companies involved in the actual construction of structures or civil

engineering projects control more subsidiaries than firms operational in the architectural and

engineering consultancy sectors. From a geographical point of view, the lion share of the subsidiaries

is still located in the domestic country of the parent firm. Besides this domestic country the other

fellow EU member states prove attractive as well. Only 13% of the subsidiaries are located outside

Europe. Besides the home country of the parent firm, countries with a common history or language

55

appeared more attractive for foreign investment than unrelated countries. The subsidiary intensity is

much higher in the domestic country of the mother company with about 25 subsidiaries compared to

their presence abroad: the other regions can on average only count on 2 to 6 subsidiaries.

The subsidiary-based approach has strong limitations. Approximations for the real export values have

to be used, which of course do not represent the true numbers perfectly. Despite these limitations

though, the approximation allows for some interesting insights. Given the importance of the AEC

sector in an increasingly globalized world and the limited literature on qualitative assessment of what

drives internationalization, this field remains a fascinating domain for future research. Later research

might try to verify the effects of internationalization on firm profitability and indebtedness through

actual export data, while the drivers of internationalization as well as other aspects of

internationalization still deserve more thorough research.

Lastly, throughout this study it has been shown that the global financial crisis of 2008 had a non-

negligible effect on the construction sector in Europe. This fact and the financial vulnerability of the

industry also call for further investigation.

57

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Annex 1.1

ANNEXES

Annex 1: Size distribution of sample firms by industry

SIZE CATEGORY NACE F.41 NACE F.42 NACE M.71.1.1 NACE M.71.1.2

Medium sized 4.95% 3.39% 26.09% 12.73%

Large 33.66% 22.03% 34.78% 38.18%

Very large 61.39% 74.58% 39.13% 49.09%

Total 100% 100% 100% 100%

Annex 2.1

Annex 2: Emerging economies

Country Economy group

Brazil BRIC

China BRIC

India BRIC

Russia BRIC

Country Economy group

Argentina VISTA

Australia MIKTA

Bangladesh Next 11

Colombia CIVETS

Egypt Next 11, CIVETS

Indonesia Next 11, CIVETS, MINT, VISTA, MIKTA

Iran Next 11

Mexico Next 11, MINT, MIKTA

Nigeria Next 11, MINT

Pakistan Next 11

Philippines Next 11

South Africa CIVETS, VISTA

South Korea Next 11, MIKTA

Turkey Next 11, CIVETS, MINT, VISTA, MIKTA

Vietnam Next 11, CIVETS, VISTA

Annex 3.1

Annex 3: Geographical spread of subsidiaries in the different UN

subregions

Subregion Absolute amount

Percentage Region Absolute amount

Percentage

Main mother country 7836 71.74%

Europe 9485 86.84%

Other fellow EU 2003 countries

982 8.99%

New EU countries 2003-2013

478 4.38%

Other Europe 189 1.73%

Northern America 295 2.70%

America 793 7.26% Central America 186 1.70%

Caribbean 17 0.16%

South America 295 2.70%

Northern Africa 72 0.66%

Africa 235 2.15%

Western Africa 35 0.32%

Middle Africa 57 0.52%

Eastern Africa 38 0.35%

Southern Africa 33 0.30%

Western Asia 84 0.77%

Asia 234 2.14%

Central Asia 8 0.07%

Southern Asia 31 0.28%

Southeastern Asia 63 0.58%

Eastern Asia 48 0.44%

Australia & New Zealand

54 0.49% Oceania 54 0.49%

Pacific 0 0.00%

Unknown 122 1.12% Unknown 122 1.12%

TOTAL 10,923 100% TOTAL 10,923 100%

Annex 3.2

Annex 4.1

Annex 4: Comparison of export data and subsidiary data – regression

results

VARIABLE COEFFICIENT STD. ERROR T-STATISTIC PROB. C -0.236077 0.358387 -0.658719 0.5119

Std. Wsr. TNI† 2.969320 1.854611 1.601047 0.1132

Periods included 9

Cross-sections incuded 27

Total panel observations 111

R-squared 0.724839

Adjusted R-squared 0.635329

S.E. of regression 0.738966

Sum squared resid 45.32386

Log likelihood -107.7910

F-statistic 8.097834

Prob(F-statistic) 0.000000

Mean dependent var 0.329771

S.D. dependent var 1.223696

Akaike info criterion 2.446685

Schwarz criterion 3.130170

Hannan-Quinn criter. 2.723955

Durbin-Watson stat 2.554128

VARIABLE COEFFICIENT STD. ERROR T-STATISTIC PROB. C 0.348237 0.073364 4.746699 0.0000

Std. Wsr. TNI†ep 0.442878 0.155146 2.854583 0.0054

Periods included 9



Annex 4.2

R-squared 0.733180














Std. Wsr. TASI† 2.522123 0.794433 3.174744 0.0021

Periods included 9



R-squared 0.748779














Std. Wsr. TASI†ep 5.259425 1.675254 3.139479 0.0023

Annex 4.3

Periods included 9



R-squared 0.738723














Std. Wsr. FSTS† 0.543730 0.245086 2.218527 0.0293

Std. Wsr. FATA† -0.006056 0.194648 -0.031111 0.9753

Std. Wsr. FETE† 1.178048 2.546889 0.462544 0.6449

Periods included 9



R-squared 0.767068













Annex 4.4


Std. Wsr. FSTS†ep 1.591610 0.771315 2.063502 0.0423

Std. Wsr. FATA†ep -0.434058 0.869594 -0.499150 0.6190

Std. Wsr. FETE†ep 1.590824 2.323073 0.684793 0.4954

Periods included 9



R-squared 0.757466














Std. Wsr. FSTS† x NSI† 5.402190 0.996934 5.418806 0.0000

Std. Wsr. FATA† x NSI† -3.360695 1.723562 -1.949854 0.0547

Std. Wsr. FETE† x NSI† 6.279525 1.478901 4.246074 0.0001

Periods included 9



Annex 4.5

R-squared 0.837567














Std. Wsr. FSTS†ep x NSI† 18.28687 2.252404 8.118824 0.0000

Std. Wsr. FATA† ep x NSI† -4.138725 2.768529 -1.494919 0.1388

Std. Wsr. FETE† ep x NSI†

1.587064 0.420621 3.773144 0.0003

Periods included 9



R-squared 0.854546













Annex 5.1

Annex 5: Profitability relation – descriptive statistics

ROA [-] ROA_IND [-] ROA_LOC [-] FSTS [-] FSTSep [-]

Mean 0.047371 0.045842 0.045714 0.150604 0.761342

Median 0.046808 0.045763 0.044377 0.063004 0.167589

Maximum 0.323427 0.123925 0.138499 1.975415 17.57423

Minimum -0.261234 -0.004467 -0.025060 0.000000 0.000000

Std. Dev. 0.077687 0.027513 0.032998 0.243050 2.151990

Skewness -0.304908 0.885839 0.603738 3.461980 6.194725

Kurtosis 7.292028 3.476988 3.968024 18.53134 44.34528

Jarque-Bera 500.3737 89.62936 63.76867 7698.985 49600.54

Probability 0.000000 0.000000 0.000000 0.000000 0.000000

Sum 30.27020 29.29326 29.21137 96.23585 486.4977

Sum Sq. Dev. 3.850456 0.482949 0.694714 37.68865 2954.618

Observations 639 639 639 639 639

FSTS x NSI [-] (FSTS x NSI) ep [-] TNI [-] TNIep [-] TASI [-] TASIep [-]

Mean 0.011191 0.075373 0.147939 0.728502 0.010410 0.081197

Median 0.001484 0.004153 0.081627 0.242912 0.001781 0.005668

Maximum 0.284888 2.308299 0.949508 24.38093 0.236833 2.840560

Minimum 0.000000 0.000000 6.19E-05 0.000145 1.35E-06 1.83E-06

Std. Dev. 0.036627 0.288527 0.182054 1.808144 0.028643 0.332176

Skewness 6.562998 6.769154 1.984226 7.849228 6.023368 7.442769

Kurtosis 48.14825 51.29596 6.880003 82.85577 43.47378 60.16212

Jarque-Bera 58858.73 66982.79 820.1309 176347.7 47479.05 92896.94

Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

Sum 7.150906 48.16309 94.53279 465.5129 6.652115 51.88497

Sum Sq. Dev. 0.855908 53.11206 21.14574 2085.867 0.523419 70.39732

Observations 639 639 639 639 639 639

ITA [-] SIZE [log(th EUR)] TDA [-] AGE [years] CRIS [%]

Mean 0.044750 12.81725 0.727287 40.23318 0.680861

Median 0.014568 12.87974 0.740838 31.00000 1.540177

Maximum 0.514584 16.53852 1.194215 105.0000 6.309729

Minimum 0.000000 7.512929 0.064504 0.000000 -8.269037

Std. Dev. 0.070362 2.003280 0.165152 30.36699 3.082803

Skewness 2.754842 -0.298448 -0.440348 0.734353 -0.984823

Kurtosis 12.87838 2.760661 4.131131 2.276508 3.331996

Jarque-Bera 3406.376 11.01127 54.71655 71.36927 106.2265

Probability 0.000000 0.004064 0.000000 0.000000 0.000000

Sum 28.59504 8190.220 464.7363 25709.00 435.0702

Sum Sq. Dev. 3.158601 2560.379 17.40161 588334.3 6063.344

Observations 639 639 639 639 639

Annex 5.2

Covariance RO

A

FSTS

FSTS

ep

FSTS

x N

SI

(FST

S x

NSI

) ep

TNI

TNI e

p

TASI

TASI

ep

ITA

SIZE

TDA

AG

E

CR

IS

RO

A_I

ND

RO

A_L

OC

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1 0.0

1

2 0.0

0

0.0

6

3

-0.0

2

0.4

0

4.6

2

4 0.0

0

0.0

1

0.0

7

0.0

0

5 0.0

0

0.0

4

0.5

8

0.0

1

0.0

8

6 0.0

0

0.0

4

0.2

2

0.0

0

0.0

2

0.0

3

7

-0.0

1

0.2

9

3.5

0

0.0

6

0.4

5

0.1

9

3.2

6

8 0.0

0

0.0

0

0.0

5

0.0

0

0.0

1

0.0

0

0.0

4

0.0

0

9 0.0

0

0.0

4

0.6

5

0.0

1

0.0

9

0.0

3

0.5

2

0.0

1

0.1

1

10 0.0

0

0.0

0

0.0

1

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

11

-0.0

1

0.0

2

1.0

4

0.0

2

0.1

8

0.0

0

0.8

9

0.0

2

0.2

0

0.0

2

4.0

1

12 0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

1

0.0

0

0.0

0

0.0

0

0.0

4

0.0

3

13 0.1

4

0.1

6

6.8

5

0.2

3

1.6

6

0.0

6

5.8

3

0.2

0

1.8

2

0.1

8

31

.99

0.0

3

92

0.7

1

14 0.0

4

0.0

9

0.7

2

0.0

1

0.0

7

0.0

5

0.6

6

0.0

1

0.0

7

-0.0

1

0.0

6

-0.0

1

3.7

0

9.4

9

15 0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

-0.0

1

0.0

0

-0.1

0

0.0

2

0.0

0

16 0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

4

0.0

4

0.0

0

0.0

0

Annex 5.3

Corre-lation

RO

A

FSTS

FSTS

ep

FSTS

x N

SI

(FST

S x

NSI

) ep

TNI

TNI e

p

TASI

TASI

ep

ITA

SIZE

TDA

AG

E

CR

IS

RO

A_I

ND

RO

A_L

OC

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1 1.0

0

2

-0.0

7

1.0

0

3

-0.0

9

0.7

6

1.0

0

4

-0.0

4

0.6

9

0.9

0

1.0

0

5

-0.0

4

0.6

0

0.9

4

0.9

5

1.0

0

6

-0.0

1

0.8

8

0.5

7

0.5

5

0.4

4

1.0

0

7

-0.0

4

0.6

5

0.9

0

0.8

3

0.8

6

0.5

7

1.0

0

8

-0.0

2

0.6

6

0.8

6

0.9

7

0.9

2

0.5

9

0.8

4

1.0

0

9

-0.0

2

0.5

6

0.9

1

0.9

4

0.9

8

0.4

3

0.8

7

0.9

3

1.0

0

10

-0.0

5

0.0

4

0.0

4

0.0

8

0.0

6

0.0

2

0.0

3

0.0

8

0.0

4

1.0

0

11

-0.0

8

0.0

4

0.2

4

0.3

0

0.3

2

0.0

0

0.2

5

0.3

3

0.3

1

0.1

4

1.0

0

12

-0.1

8

-0.0

7

0.0

1

0.0

4

0.0

5

-0.0

5

0.0

4

0.0

4

0.0

6

0.0

0

0.1

3

1.0

0

13 0.0

6

0.0

2

0.1

1

0.2

0

0.1

9

0.0

1

0.1

1

0.2

4

0.1

8

0.0

8

0.5

3

0.0

1

1.0

0

14 0.1

7

0.1

1

0.1

1

0.0

8

0.0

8

0.0

9

0.1

2

0.0

7

0.0

7

-0.0

3

0.0

1

-0.0

2

0.0

4

1.0

0

15 0.3

4

0.0

8

0.0

4

0.0

1

0.0

2

0.0

9

0.0

6

0.0

2

0.0

3

0.1

0

-0.1

4

0.0

0

-0.1

1

0.2

0

1.0

0

16 0.2

9

0.1

0

0.0

0

0.0

2

-0.0

1

0.1

3

0.0

5

0.0

3

0.0

0

0.0

3

-0.0

7

-0.0

4

0.0

4

0.4

0

0.3

2

1.0

0

Annex 6.1

Annex 6: Profitability relation – regression results


Std. Wsr. FSTS† -0.657518 0.142592 -4.611179 0.0000

Std. Wsr. FSTS† ² 1.886858 0.564116 3.344806 0.0009

Std. Wsr. FSTS† ³ -1.438342 0.506582 -2.839309 0.0047

Std. Wsr. ITA 0.018435 0.080492 0.229025 0.8189

Std. Wsr. ROA_IND 0.154510 0.062627 2.467158 0.0140

Std. Wsr. ROA_LOC 0.145408 0.160815 0.904192 0.3663

Std. Wsr. SIZE -0.343177 0.056144 -6.112406 0.0000

Std. Wsr. TDA -0.013540 0.689963 -0.019624 0.9844

Std. Wsr. AGE 0.177653 0.059423 2.989641 0.0029

Std. Wsr. CRIS 0.037301 0.027515 1.355643 0.1758

Periods included 9



R-squared 0.716679













Annex 6.2


Std. Wsr. FSTS†ep -0.230492 0.141885 -1.624504 0.1049

Std. Wsr. FSTS†ep ² 0.109228 0.482565 0.226350 0.8210

Std. Wsr. FSTS†ep ³ -0.082712 0.313245 -0.264049 0.7919

Std. Wsr. ITA 0.036071 0.077537 0.465206 0.6420

Std. Wsr. ROA_IND 0.153699 0.061985 2.479620 0.0135

Std. Wsr. ROA_LOC 0.108998 0.159945 0.681470 0.4959

Std. Wsr. SIZE -0.332349 0.057724 -5.757591 0.0000

Std. Wsr. TDA -0.151032 0.659380 -0.229051 0.8189

Std. Wsr. AGE 0.186593 0.056749 3.288018 0.0011

Std. Wsr. CRIS 0.033215 0.025586 1.298193 0.1948

Periods included 9



R-squared 0.713724













Annex 6.3


Std. Wsr. FSTS† x NSI† -0.050832 0.195221 -0.260384 0.7947

Std. Wsr. (FSTS† x NSI†) ² -0.810716 0.973131 -0.833100 0.4052

Std. Wsr. (FSTS† x NSI†) ³ 0.808000 0.796623 1.014282 0.3110

Std. Wsr. ITA 0.040065 0.075402 0.531347 0.5954

Std. Wsr. ROA_IND 0.156089 0.059713 2.613964 0.0092

Std. Wsr. ROA_LOC 0.123041 0.156507 0.786166 0.4322

Std. Wsr. SIZE -0.329966 0.059150 -5.578426 0.0000

Std. Wsr. TDA -0.023005 0.665414 -0.034573 0.9724

Std. Wsr. AGE 0.183858 0.060928 3.017645 0.0027

Std. Wsr. CRIS 0.033120 0.026706 1.240161 0.2155

Periods included 9



R-squared 0.711210













Annex 6.4


Std. Wsr. FSTS†ep x NSI† -0.837814 0.465522 -1.799732 0.0725

Std. Wsr. (FSTS†ep x NSI†) ² 4.030515 2.787949 1.445691 0.1489

Std. Wsr. (FSTS†ep x NSI†) ³ -8.530817 6.105920 -1.397139 0.1630

Std. Wsr. ITA 0.045636 0.075245 0.606508 0.5445

Std. Wsr. ROA_IND 0.159646 0.058563 2.726038 0.0066

Std. Wsr. ROA_LOC 0.080920 0.165619 0.488591 0.6254

Std. Wsr. SIZE -0.326544 0.060397 -5.406582 0.0000

Std. Wsr. TDA -0.120330 0.634710 -0.189583 0.8497

Std. Wsr. AGE 0.179457 0.056143 3.196446 0.0015

Std. Wsr. CRIS 0.032879 0.025800 1.274382 0.2031

Periods included 9



R-squared 0.713555













Annex 6.5


Std. Wsr. TNI† -3.074131 1.785356 -1.721858 0.0857

Std. Wsr. TNI† ² 60.31914 54.76391 1.101440 0.2713

Std. Wsr. TNI† ³ -345.9829 449.2869 -0.770071 0.4416

Std. Wsr. ITA 0.048194 0.070400 0.684569 0.4939

Std. Wsr. ROA_IND 0.163186 0.058054 2.810959 0.0051

Std. Wsr. ROA_LOC 0.013155 0.170723 0.077053 0.9386

Std. Wsr. SIZE -0.315408 0.065010 -4.851673 0.0000

Std. Wsr. TDA 0.162333 0.652044 0.248961 0.8035

Std. Wsr. AGE 0.187615 0.055364 3.388736 0.0008

Std. Wsr. CRIS 0.024915 0.024295 1.025494 0.3056

Periods included 9



R-squared 0.710417













Annex 6.6


Std. Wsr. TNI†ep -0.137000 0.200286 -0.684022 0.4943

Std. Wsr. TNI†ep ² 0.072927 0.589353 0.123740 0.9016

Std. Wsr. TNI†ep ³ -0.009981 0.477831 -0.020888 0.9833

Std. Wsr. ITA 0.047534 0.067687 0.702262 0.4829

Std. Wsr. ROA_IND 0.159997 0.057529 2.781173 0.0056

Std. Wsr. ROA_LOC 0.082086 0.172576 0.475654 0.6345

Std. Wsr. SIZE -0.324684 0.061451 -5.283617 0.0000

Std. Wsr. TDA -0.017632 0.644489 -0.027359 0.9782

Std. Wsr. AGE 0.193064 0.055195 3.497843 0.0005

Std. Wsr. CRIS 0.028120 0.024605 1.142857 0.2537

Periods included 9



R-squared 0.708996













Annex 6.7


Std. Wsr. TASI† -0.221997 0.235846 -0.941281 0.3470

Std. Wsr. TASI† ² 0.391835 0.425482 0.920921 0.3576

Std. Wsr. TASI† ³ -0.210572 0.271168 -0.776537 0.4378

Std. Wsr. ITA 0.047149 0.066415 0.709914 0.4781

Std. Wsr. ROA_IND 0.162441 0.055944 2.903652 0.0039

Std. Wsr. ROA_LOC 0.092886 0.172027 0.539950 0.5895

Std. Wsr. SIZE -0.322453 0.063871 -5.048539 0.0000

Std. Wsr. TDA 0.031715 0.644815 0.049184 0.9608

Std. Wsr. AGE 0.188679 0.057243 3.296112 0.0011

Std. Wsr. CRIS 0.027144 0.025311 1.072423 0.2841

Periods included 9



R-squared 0.708825













Annex 6.8


Std. Wsr. TASI†ep 0.072956 0.550386 0.132554 0.8946

Std. Wsr. TASI†ep ² -2.228165 1.894873 -1.175891 0.2402

Std. Wsr. TASI†ep ³ 1.457640 1.137849 1.281049 0.2008

Std. Wsr. ITA 0.056948 0.068856 0.827059 0.4086

Std. Wsr. ROA_IND 0.161894 0.058792 2.753695 0.0061

Std. Wsr. ROA_LOC 0.073700 0.175711 0.419436 0.6751

Std. Wsr. SIZE -0.325444 0.062707 -5.189877 0.0000

Std. Wsr. TDA -0.047273 0.609835 -0.077518 0.9382

Std. Wsr. AGE 0.187593 0.051299 3.656872 0.0003

Std. Wsr. CRIS 0.028953 0.024057 1.203527 0.2294

Periods included 9



R-squared 0.711063













Annex 7.1

Annex 7: Indebtedness relations – descriptive statistics

TDA [-] LDA [-] SDA [-] ROE2 [-] ΔS2 [%] SIZE [log(th EUR)]

Mean 0.7310 0.2397 0.4913 10.2654 0.3980 12.8931

Median 0.7538 0.2154 0.4926 16.8885 0.1408 12.9023

Maximum 0.9930 0.7350 0.8930 241.3405 39.4619 17.1644

Minimum 0.1097 0.0047 0.0809 -518.9505 -0.9886 7.1671

Std. Dev. 0.1366 0.1596 0.1605 51.6415 2.8378 1.9176

Skewness -0.9163 0.7054 0.0706 -5.8703 11.8325 -0.1108

Kurtosis 4.2741 2.8370 2.6749 56.7812 150.5253 2.6556

Jarque-Bera 70.3657 28.4863 1.7747 42802.4000 315322.9000 2.3696

Probability 0.0000 0.0000 0.4118 0.0000 0.0000 0.3058

Sum 247.8145 81.2678 166.5466 3479.9820 134.9247 4370.7520

Sum Sq. Dev. 6.3113 8.6141 8.7044 901394.600

0 2721.9950 1242.8490

Observations 339 339 339 339 339 339

TASI [-] TASIep [-] TNI [-] TNIep [-] AGE [years] CRIS [%]

Mean 0.0129 0.1117 0.1466 0.7209 45.8555 0.5938

Median 0.0016 0.0074 0.0789 0.2367 36.0000 1.4741

Maximum 0.2608 5.0145 0.9495 15.5289 135.0000 5.9889

Minimum 0.0000 0.0000 0.0000 0.0001 2.0000 -9.1325

Std. Dev. 0.0344 0.4792 0.1874 1.5727 31.5720 3.3895

Skewness 5.0522 7.2556 1.8916 5.6455 0.6854 -0.9785

Kurtosis 30.6523 59.7397 6.2785 42.1679 2.3384 3.1934

Jarque-Bera 12242.8200 48448.3100 353.9870 23470.2000 32.7216 54.6291

Probability 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Sum 4.3826 37.8571 49.7027 244.3997 15545.0000 201.2955

Sum Sq. Dev. 0.4010 77.6223 11.8764 836.0262 336915.9000 3883.1160

Observations 339 339 339 339 339 339

TDA_IND [-]

TDA_LOC [-]

LDA_IND [-]

LDA_LOC [-]

SDA_IND [-]

SDA_LOC [-]

Mean 0.7317 0.7458 0.2392 0.2367 0.4924 0.5091

Median 0.7389 0.7449 0.2570 0.2152 0.4967 0.5256

Maximum 0.7803 0.9010 0.2903 0.5184 0.6019 0.6502

Minimum 0.5609 0.5443 0.1194 0.1013 0.4313 0.2491

Std. Dev. 0.0256 0.0636 0.0397 0.0925 0.0421 0.0721

Skewness -1.3462 -0.4158 -0.9695 0.5614 0.6872 -0.3386

Kurtosis 10.7532 3.1829 2.9971 2.4122 2.8840 2.6615

Jarque-Bera 951.4751 10.2385 53.1044 22.6905 26.8688 8.0975

Probability 0.0000 0.0060 0.0000 0.0000 0.0000 0.0174

Sum 248.0334 252.8115 81.0966 80.2405 166.9368 172.5710

Sum Sq. Dev. 0.2217 1.3685 0.5327 2.8893 0.5991 1.7564

Observations 339 339 339 339 339 339

Annex 7.2

Covariance TDA

LDA

SDA

TASI

TASI

ep

TNI

TNI e

p

RO

E2

ΔS2

SIZE

AG

E

CR

IS

TDA

_IN

D

TDA

_LO

C

LDA

_IN

D

LDA

_LO

C

SDA

_IN

D

SDA

_LO

C

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1 0.0

2

2 0.0

1

0.0

3

3 0.0

1

-0.0

2

0.0

3

4 0.0

0

0.0

0

0.0

0

0.0

1

5 0.0

0

0.0

1

0.0

0

0.0

6

0.7

4

6

-0.0

1

-0.0

1

-0.0

1

0.0

5

0.4

7

0.5

3

7

-0.0

3

0.0

0

-0.0

3

0.4

8

5.4

7

4.3

8

46

.85

8

-1.4

6

-2.4

4

0.9

9

0.2

4

2.4

4

1.2

1

15

.39

26

11

.79

9 0.0

2

0.0

1

0.0

1

0.0

0

-0.0

2

-0.0

1

-0.1

1

-14

.43

7.9

7

10 0.0

6

0.0

6

-0.0

1

0.0

3

0.3

7

0.0

4

1.4

6

12

.83

0.0

3

3.6

4

11 0.4

4

1.0

4

-0.6

0

0.1

2

0.7

4

-1.2

9

-7.9

1

21

0.4

6

-6.6

3

32

.07

99

0.7

4

12

-0.0

1

-0.0

3

0.0

2

0.0

2

0.1

6

0.1

8

1.1

9

34

.14

-0.3

0

0.7

5

5.5

2

11

.35

13 0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

1

-0.1

2

0.0

0

0.0

0

-0.0

2

0.0

0

0.0

0

14

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

-0.0

1

-0.0

2

-0.6

3

0.0

0

0.0

1

0.1

9

-0.0

4

0.0

0

0.0

0

15 0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

-0.2

7

0.0

0

0.0

1

0.2

0

0.0

1

0.0

0

0.0

0

0.0

0

16 0.0

0

0.0

1

-0.0

1

0.0

0

0.0

0

-0.0

1

-0.0

2

-0.5

6

-0.0

1

0.0

1

0.6

1

-0.0

3

0.0

0

0.0

0

0.0

0

0.0

1

17 0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

2

0.1

5

0.0

0

-0.0

2

-0.2

2

-0.0

1

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

18 0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

-0.0

7

0.0

1

-0.0

1

-0.4

3

-0.0

1

0.0

0

0.0

0

0.0

0

0.0

0

0.0

0

0.0

1

Annex 7.3

Corre-lation

TDA

LDA

SDA

TASI

TASI

ep

TNI

TNI e

p

RO

E2

ΔS2

SIZE

AG

E

CR

IS

TDA

_IN

D

TDA

_LO

C

LDA

_IN

D

LDA

_LO

C

SDA

_IN

D

SDA

_LO

C

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

1

1.0

0

2

0.4

3

1.0

0

3

0.4

4

-0.6

2

1.0

0

4

-0.0

4

0.0

1

-0.0

5

1.0

0

5

0.0

3

0.0

4

-0.0

2

0.9

3

1.0

0

6

-0.1

2

-0.0

5

-0.0

5

0.9

1

0.7

5

1.0

0

7

-0.0

3

0.0

0

-0.0

2

0.9

4

0.9

3

0.8

8

1.0

0

8

-0.2

1

-0.3

0

0.1

2

0.0

6

0.0

6

0.0

3

0.0

4

1.0

0

9

0.0

5

0.0

1

0.0

3

-0.0

1

-0.0

1

-0.0

1

-0.0

1

-0.1

0

1.0

0

10

0.2

1

0.2

1

-0.0

2

0.2

1

0.2

2

0.0

3

0.1

1

0.1

3

0.0

1

1.0

0

11

0.1

0

0.2

1

-0.1

2

0.0

5

0.0

3

-0.0

6

-0.0

4

0.1

3

-0.0

7

0.5

3

1.0

0

12

-0.0

2

-0.0

5

0.0

3

0.0

7

0.0

5

0.0

7

0.0

5

0.2

0

-0.0

3

0.1

2

0.0

5

1.0

0

13

0.1

2

0.0

7

0.0

4

0.0

8

0.0

7

0.1

0

0.0

8

-0.1

0

0.0

3

-0.0

5

-0.0

3

-0.0

3

1.0

0

14

0.3

3

0.3

1

-0.0

2

-0.0

6

0.0

0

-0.1

3

-0.0

5

-0.1

9

0.0

1

0.0

6

0.0

9

-0.1

8

0.1

6

1.0

0

15

0.0

8

0.2

2

-0.1

5

-0.0

2

0.0

0

-0.0

5

0.0

0

-0.1

3

-0.0

2

0.1

6

0.1

6

0.0

6

0.2

2

0.1

0

1.0

0

16

0.2

0

0.5

4

-0.3

6

0.0

0

0.0

2

-0.0

8

-0.0

4

-0.1

2

-0.0

2

0.0

8

0.2

1

-0.0

9

0.0

6

0.6

3

0.1

0

1.0

0

17

0.0

0

-0.1

6

0.1

6

0.0

7

0.0

4

0.1

0

0.0

5

0.0

7

0.0

3

-0.1

9

-0.1

6

-0.0

8

0.4

0

0.0

1

-0.8

1

-0.0

6

1.0

0

18

0.0

3

-0.4

1

0.4

3

-0.0

5

-0.0

2

-0.0

2

0.0

0

-0.0

2

0.0

3

-0.0

5

-0.1

9

-0.0

5

0.0

7

0.0

8

-0.0

5

-0.7

3

0.0

9

1.0

0

Annex 8.1

Annex 8: Indebtedness relation – regression results


Std. Wsr. TASI† -0.121758 0.061889 -1.967371 0.0503

Std. Wsr. ROE2 -0.089255 0.018027 -4.951219 0.0000

Std. Wsr. ΔS2 -0.034103 0.024559 -1.388630 0.1662

Std. Wsr. SIZE 0.358740 0.145686 2.462430 0.0145

Std. Wsr. AGE -0.959391 0.157040 -6.109216 0.0000

Std. Wsr. CRIS -0.003308 0.005273 -0.627443 0.5310

Std. Wsr. TDA_IND 0.036264 0.027196 1.333420 0.1836

Std. Wsr. TDA_LOC 0.080204 0.022565 3.554275 0.0005

Periods included 7



R-squared 0.905987




Log likelihood 37.87200









Annex 8.2


Std. Wsr. TASI†ep -0.101777 0.067764 -1.501926 0.1344

Std. Wsr. ROE2 -0.095683 0.020874 -4.583921 0.0000

Std. Wsr. ΔS2 -0.034863 0.024922 -1.398902 0.1631

Std. Wsr. SIZE 0.429610 0.143245 2.999133 0.0030

Std. Wsr. AGE -1.054448 0.141548 -7.449402 0.0000

Std. Wsr. CRIS -0.004210 0.006738 -0.624704 0.5328

Std. Wsr. TDA_IND 0.029263 0.027367 1.069297 0.2860

Std. Wsr. TDA_LOC 0.088436 0.018268 4.841176 0.0000

Periods included 7



R-squared 0.903591













Annex 8.3


Std. Wsr. TNI† -0.121684 0.076212 -1.596637 0.1117

Std. Wsr. ROE2 -0.089449 0.015500 -5.771026 0.0000

Std. Wsr. ΔS2 -0.035721 0.025142 -1.420761 0.1567

Std. Wsr. SIZE 0.381071 0.135422 2.813942 0.0053

Std. Wsr. AGE -1.026886 0.149925 -6.849345 0.0000

Std. Wsr. CRIS -0.005014 0.006934 -0.723084 0.4703

Std. Wsr. TDA_IND 0.033335 0.026226 1.271102 0.2049

Std. Wsr. TDA_LOC 0.089518 0.016902 5.296187 0.0000

Periods included 7



R-squared 0.905736













Annex 8.4


Std. Wsr. TNI†ep -0.112947 0.064346 -1.755297 0.0805

Std. Wsr. ROE2 -0.087560 0.016434 -5.328044 0.0000

Std. Wsr. ΔS2 -0.037633 0.025844 -1.456194 0.1466

Std. Wsr. SIZE 0.363163 0.138320 2.625531 0.0092

Std. Wsr. AGE -1.083335 0.143774 -7.534961 0.0000

Std. Wsr. CRIS -0.003726 0.006047 -0.616220 0.5383

Std. Wsr. TDA_IND 0.035103 0.024749 1.418378 0.1574

Std. Wsr. TDA_LOC 0.087734 0.017736 4.946666 0.0000

Periods included 7



R-squared 0.905935













Annex 8.5


Std. Wsr. TASI† -0.080498 0.035014 -2.299044 0.0224

Std. Wsr. ROE2 -0.189666 0.051984 -3.648561 0.0003

Std. Wsr. ΔS2 -0.038686 0.022466 -1.721955 0.0864

Std. Wsr. SIZE 0.307587 0.165996 1.852976 0.0651

Std. Wsr. AGE -0.090237 0.385988 -0.233781 0.8154

Std. Wsr. CRIS -0.029403 0.014148 -2.078253 0.0387

Std. Wsr. LDA_IND 0.074736 0.032082 2.329524 0.0207

Std. Wsr. LDA _LOC 0.219577 0.048188 4.556703 0.0000

Periods included 7



R-squared 0.843311













Annex 8.6


Std. Wsr. TASI†ep -0.058565 0.021878 -2.676897 0.0079

Std. Wsr. ROE2 -0.194525 0.050502 -3.851851 0.0002

Std. Wsr. ΔS2 -0.038540 0.022306 -1.727808 0.0853

Std. Wsr. SIZE 0.360813 0.170533 2.115795 0.0354

Std. Wsr. AGE -0.174109 0.420830 -0.413729 0.6794

Std. Wsr. CRIS -0.030464 0.014850 -2.051439 0.0413

Std. Wsr. LDA _IND 0.070108 0.032606 2.150162 0.0325

Std. Wsr. LDA _LOC 0.223746 0.050757 4.408154 0.0000

Periods included 7



R-squared 0.842404













Annex 8.7


Std. Wsr. TNI† -0.044334 0.021159 -2.095258 0.0372

Std. Wsr. ROE2 -0.193933 0.050045 -3.875133 0.0001

Std. Wsr. ΔS2 -0.037878 0.022150 -1.710047 0.0885

Std. Wsr. SIZE 0.354314 0.171347 2.067816 0.0397

Std. Wsr. AGE -0.172528 0.420265 -0.410523 0.6818

Std. Wsr. CRIS -0.030963 0.015074 -2.054044 0.0410

Std. Wsr. LDA _IND 0.067043 0.033289 2.013976 0.0451

Std. Wsr. LDA _LOC 0.227227 0.050102 4.535275 0.0000

Periods included 7



R-squared 0.842205













Annex 8.8


Std. Wsr. TNI†ep -0.029541 0.018644 -1.584502 0.1144

Std. Wsr. ROE2 -0.194554 0.050294 -3.868322 0.0001

Std. Wsr. ΔS2 -0.037928 0.022134 -1.713552 0.0879

Std. Wsr. SIZE 0.359550 0.170282 2.111496 0.0358

Std. Wsr. AGE -0.189678 0.413687 -0.458506 0.6470

Std. Wsr. CRIS -0.030513 0.014797 -2.062015 0.0403

Std. Wsr. LDA _IND 0.067134 0.033027 2.032674 0.0432

Std. Wsr. LDA _LOC 0.227496 0.050325 4.520556 0.0000

Periods included 7



R-squared 0.841995













Annex 8.9


Std. Wsr. TASI† -0.322088 0.164677 -1.955877 0.0516

Std. Wsr. TASI† ² 0.228825 0.137365 1.665820 0.0970

Std. Wsr. ROE2 0.093774 0.050489 1.857325 0.0645

Std. Wsr. ΔS2 0.003778 0.021795 0.173347 0.8625

Std. Wsr. SIZE 0.004400 0.080789 0.054464 0.9566

Std. Wsr. AGE -0.779876 0.163993 -4.755556 0.0000

Std. Wsr. CRIS 0.027555 0.012407 2.220954 0.0273

Std. Wsr. SDA_IND 0.054894 0.024889 2.205558 0.0284

Std. Wsr. SDA _LOC 0.152403 0.041686 3.655928 0.0003

Periods included 7



R-squared 0.833886













Annex 8.10


Std. Wsr. TASI†ep -0.167076 0.445286 -0.375210 0.7078

Std. Wsr. TASI†ep ² 0.122890 0.429112 0.286383 0.7748

Std. Wsr. ROE2 0.092612 0.047836 1.936026 0.0540

Std. Wsr. ΔS2 -0.004999 0.024461 -0.204361 0.8382

Std. Wsr. SIZE 0.023884 0.161023 0.148326 0.8822

Std. Wsr. AGE -0.815093 0.153712 -5.302732 0.0000

Std. Wsr. CRIS 0.027499 0.011993 2.292933 0.0227

Std. Wsr. SDA _IND 0.046970 0.029864 1.572771 0.1171

Std. Wsr. SDA _LOC 0.160233 0.042066 3.809063 0.0002

Periods included 7



R-squared 0.832391













Annex 8.11


Std. Wsr. TNI† -0.920421 0.344597 -2.671006 0.0081

Std. Wsr. TNI† ² 0.859700 0.341345 2.518568 0.0124

Std. Wsr. ROE2 0.109058 0.044943 2.426592 0.0160

Std. Wsr. ΔS2 0.000134 0.023615 0.005685 0.9955

Std. Wsr. SIZE -0.164944 0.132875 -1.241347 0.2157

Std. Wsr. AGE -0.778071 0.184774 -4.210931 0.0000

Std. Wsr. CRIS 0.024371 0.012208 1.996335 0.0470

Std. Wsr. SDA _IND 0.060458 0.025573 2.364144 0.0189

Std. Wsr. SDA _LOC 0.156008 0.039974 3.902733 0.0001

Periods included 7



R-squared 0.838062













Annex 8.12


Std. Wsr. TNI†ep -0.358450 0.180957 -1.980852 0.0487

Std. Wsr. TNI†ep ² 0.248864 0.148018 1.681303 0.0940

Std. Wsr. ROE2 0.103694 0.053019 1.955806 0.0516

Std. Wsr. ΔS2 -0.006200 0.024849 -0.249496 0.8032

Std. Wsr. SIZE -0.062949 0.099799 -0.630756 0.5288

Std. Wsr. AGE -0.873183 0.172215 -5.070320 0.0000

Std. Wsr. CRIS 0.027859 0.012661 2.200418 0.0287

Std. Wsr. SDA _IND 0.049237 0.027401 1.796917 0.0736

Std. Wsr. SDA _LOC 0.161080 0.037985 4.240632 0.0000

Periods included 7



R-squared 0.835138













Annex 9.1

Annex 9: Drivers of internationalization – descriptive statistics

ΔTNI t+1 [%] ΔTNI2 [%] ΔTNI2_IND [%] ΔTNI2_LOC [%] IFS2 [-] DDS2 x IFS2 [-]

Mean 17.9834 39.8137 -11.6938 -21.3650 0.700935 0.261682

Median -0.9071 8.0086 25.5681 27.5067 1.000000 0.000000

Maximum 1137.260 692.1738 138.8328 210.0363 1.000000 1.000000

Minimum -96.3785 -94.0098 -479.7193 -1520.599 0.000000 0.000000

Std. Dev. 112.3034 126.2492 159.4215 321.0883 0.458922 0.440581

Skewness 6.818188 3.402440 -2.409855 -4.269508 -0.877734 1.084370

Kurtosis 60.18455 16.38202 7.419523 20.20351 1.770417 2.175859

Jarque-Bera 30816.21 2009.681 381.2927 3289.141 40.95908 47.99524

Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

Sum 3848.456 8520.139 -2502.478 -457210.4 150.0000 56.00000

Sum Sq. Dev. 2686368 3394976 5413440 21959800 44.85981 41.34579

Observations 214 214 214 214 214 214

ΔTNI ep t+1 [%]

ΔTNI2 ep [%]

ΔTNI2_IND ep [%]

ΔTNI2_LOC ep [%] IFS2 ep [-]

DDS2 x IFS2 ep [-]

Mean 14.7377 19.8394 20.4627 24.0673 0.607477 0.308411

Median -3.2089 -0.1854 28.7742 18.0617 1.000000 0.000000

Maximum 997.0473 566.9133 59.6484 125.7320 1.000000 1.000000

Minimum -82.5641 -87.9804 -35.9683 -48.6569 0.000000 0.000000

Std. Dev. 97.3165 101.7209 29.6502 47.5103 0.489457 0.462920

Skewness 6.537729 3.366181 -0.120734 0.715424 -0.440196 0.829681

Kurtosis 57.74879 16.63996 1.558995 2.821668 1.193773 1.688370

Jarque-Bera 28251.55 2063.079 19.03532 18.53892 36.00147 39.89186

Probability 0.000000 0.000000 0.000074 0.000094 0.000000 0.000000

Sum 3153.867 4245.636 4379.023 5150.410 130.0000 66.00000

Sum Sq. Dev. 2017219 2203939 187256.2 480789.0 51.02804 45.64486

Observations 214 214 214 214 214 214

SIZE [log(-)] ΔTDA2 [%] IED2 [%] ΔPROD2 [%]

Mean 7.674630 3.2088 3.1294 -3.5359

Median 7.517993 0.2558 2.5378 2.3788

Maximum 10.95197 96.6221 15.9458 619.2669

Minimum 2.197225 -48.6494 0.0213 -466.6794

Std. Dev. 2.058239 17.9618 2.7175 96.0790

Skewness -0.180733 2.367672 2.439899 0.183760

Kurtosis 2.169694 12.35087 10.73562 18.93162

Jarque-Bera 7.312248 979.6058 745.8987 2264.402

Probability 0.025832 0.000000 0.000000 0.000000

Sum 1642.371 686.6824 669.6845 -756.6736

Sum Sq. Dev. 902.3417 68719.38 1572.92 1966239

Observations 214 214 214 214

Annex 9.2

WSE [th EUR] AGE [years] ITA [-] CRIS [%]

Mean 52.71595 47.87383 0.047222 0.704625

Median 46.43819 37.00000 0.016428 1.540177

Maximum 163.6038 106.0000 0.514584 5.988927

Minimum 13.76415 3.000000 0.000000 -7.687911

Std. Dev. 25.18806 32.64747 0.071063 3.081961

Skewness 2.255650 0.370586 2.919586 -0.896138

Kurtosis 9.276050 1.632938 15.46331 2.853488

Jarque-Bera 532.6873 21.56222 1689.084 28.83400

Probability 0.000000 0.000021 0.000000 0.000001

Sum 11281.21 10245.00 10.10549 150.7897

Sum Sq. Dev. 135135.3 227027.6 1.075638 2023.177

Observations 214 214 214 214

Annex 9.3

Covariance (Eur. subs. only) Δ

TNI t

+1

ΔTN

I2

ΔTN

I2_

IND

ΔTN

I2_L

OC

SIZE

ΔTD

A2

IED

2

ΔP

RO

D2

IFS2

DD

S2 x

IFS2

WSE

AG

E

ITA

CR

IS

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 1.2

6

2 -0

.17

1.5

9

3

-0.1

7

-0.0

5

2.5

3

4 0.2

0

0.6

3

0.9

7

10

.26

5 0.0

2

-0.3

7

0.2

8

0.1

0

4.2

2

6 0.0

0

0.0

1

0.0

4

-0.0

6

-0.0

3

0.0

3

7 0.0

0

0.0

0

0.0

0

0.0

1

-0.0

1

0.0

0

0.0

0

8

-0.0

7

-0.0

9

0.0

2

0.3

0

0.2

7

-0.0

2

0.0

0

0.9

2

9

-0.0

8

0.1

7

0.0

2

0.3

5

0.1

2

0.0

0

0.0

0

0.0

0

0.2

1

10

-0.0

4

0.2

4

-0.0

4

0.1

6

-0.0

9

0.0

0

0.0

0

-0.0

3

0.0

8

0.1

9

11

-0.3

2

2.8

3

-3.2

3

-10

.60

-26

.09

0.1

4

0.1

4

-4.3

4

-0.3

0

0.6

5

63

1.4

7

12 2.5

7

0.8

7

1.6

2

12

.55

38

.39

-1.0

0

-0.1

4

2.9

1

0.9

9

-0.7

6

-19

1.7

4

10

60

.88

13 0.0

0

-0.0

1

-0.0

1

-0.0

1

0.0

5

0.0

0

0.0

0

0.0

1

0.0

0

0.0

0

-0.2

9

0.2

5

0.0

1

14

-0.0

4

-0.2

9

-0.6

4

-0.8

7

0.9

2

-0.0

3

-0.0

2

0.6

6

0.2

9

-0.0

9

-7.8

0

5.5

3

0.0

0

9.4

5

Annex 9.4

Covariance (extrapolated) Δ

TNI t

+1

ΔTN

I2

ΔTN

I2_

IND

ΔTN

I2_L

OC

SIZE

ΔTD

A2

IED

2

ΔP

RO

D2

IFS2

DD

S2 x

IFS2

WSE

AG

E

ITA

CR

IS

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 0.9

4

2 -0

.08

1.0

3

3 0.0

0

0.1

0

0.0

9

4 0.0

4

0.2

1

0.0

6

0.2

2

5

-0.2

0

-0.4

2

-0.0

9

-0.1

5

4.2

2

6 0.0

0

0.0

0

0.0

0

0.0

0

-0.0

3

0.0

3

7 0.0

0

0.0

0

0.0

0

0.0

0

-0.0

1

0.0

0

0.0

0

8

-0.1

1

-0.2

2

-0.0

6

-0.0

7

0.2

7

-0.0

2

0.0

0

0.9

2

9

-0.0

1

0.1

5

0.0

0

0.0

2

-0.0

3

0.0

0

0.0

0

-0.0

4

0.2

4

10 0.0

2

0.1

6

0.0

1

0.0

4

-0.0

5

0.0

0

0.0

0

-0.0

1

0.1

2

0.2

1

11 2

.73

4.4

4

0.9

6

2.8

1

-26

.09

0.1

4

0.1

4

-4.3

4

0.7

8

1.2

6

63

1.4

7

12

-0.2

3

-0.5

6

-0.0

3

-0.9

2

38

.39

-1.0

0

-0.1

4

2.9

1

0.7

1

0.2

8

-19

1.7

4

10

60

.88

13

-0.0

1

-0.0

1

0.0

0

0.0

0

0.0

5

0.0

0

0.0

0

0.0

1

0.0

0

0.0

0

-0.2

9

0.2

5

0.0

1

14

-0.0

7

-0.2

3

-0.2

6

-0.3

1

0.9

2

-0.0

3

-0.0

2

0.6

6

0.1

8

-0.0

1

-7.8

0

5.5

3

0.0

0

9.4

5

Annex 9.5

Correlation (Eur. subs. only) Δ

TNI t

+1

ΔTN

I2

ΔTN

I2_

IND

ΔTN

I2_L

OC

SIZE

ΔTD

A2

IED

2

ΔP

RO

D2

IFS2

DD

S2 x

IFS2

WSE

AG

E

ITA

CR

IS

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 1.0

0

2 -0

.12

1.0

0

3

-0.1

0

-0.0

2

1.0

0

4 0.0

6

0.1

6

0.1

9

1.0

0

5 0.0

1

-0.1

4

0.0

8

0.0

2

1.0

0

6

-0.0

2

0.0

2

0.1

2

-0.1

0

-0.0

7

1.0

0

7 0.0

4

-0.0

9

-0.0

3

0.0

7

-0.2

2

0.2

0

1.0

0

8

-0.0

6

-0.0

7

0.0

1

0.1

0

0.1

4

-0.1

5

-0.1

9

1.0

0

9

-0.1

5

0.2

9

0.0

2

0.2

4

0.1

2

0.0

2

-0.0

9

0.0

0

1.0

0

10

-0.0

7

0.4

3

-0.0

6

0.1

2

-0.1

0

0.0

1

0.0

1

-0.0

6

0.3

9

1.0

0

11

-0.0

1

0.0

9

-0.0

8

-0.1

3

-0.5

1

0.0

3

0.2

1

-0.1

8

-0.0

3

0.0

6

1.0

0

12 0.0

7

0.0

2

0.0

3

0.1

2

0.5

7

-0.1

7

-0.1

6

0.0

9

0.0

7

-0.0

5

-0.2

3

1.0

0

13

-0.0

6

-0.0

8

-0.0

5

-0.0

3

0.3

5

0.0

9

-0.0

7

0.1

4

0.0

2

-0.1

1

-0.1

6

0.1

1

1.0

0

14

-0.0

1

-0.0

8

-0.1

3

-0.0

9

0.1

5

-0.0

5

-0.2

8

0.2

2

0.2

0

-0.0

7

-0.1

0

0.0

6

0.0

0

1.0

0

Annex 9.6

Correlation (extrapolated) Δ

TNI t

+1

ΔTN

I2

ΔTN

I2_

IND

ΔTN

I2_L

OC

SIZE

ΔTD

A2

IED

2

ΔP

RO

D2

IFS2

DD

S2 x

IFS2

WSE

AG

E

ITA

CR

IS

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 1.0

0

2 -0

.08

1.0

0

3 0.0

1

0.3

2

1.0

0

4 0.0

9

0.4

3

0.4

4

1.0

0

5

-0.1

0

-0.2

0

-0.1

6

-0.1

5

1.0

0

6 0.0

0

0.0

1

-0.0

7

-0.0

4

-0.0

7

1.0

0

7 0.1

1

-0.0

6

0.0

5

-0.0

7

-0.2

2

0.2

0

1.0

0

8

-0.1

1

-0.2

2

-0.2

1

-0.1

5

0.1

4

-0.1

5

-0.1

9

1.0

0

9

-0.0

2

0.3

0

0.0

3

0.0

8

-0.0

3

0.0

0

-0.0

7

-0.0

8

1.0

0

10 0.0

4

0.3

5

0.0

8

0.1

7

-0.0

5

0.0

1

0.1

3

-0.0

2

0.5

4

1.0

0

11 0

.11

0.1

7

0.1

3

0.2

4

-0.5

1

0.0

3

0.2

1

-0.1

8

0.0

6

0.1

1

1.0

0

12

-0.0

1

-0.0

2

0.0

0

-0.0

6

0.5

7

-0.1

7

-0.1

6

0.0

9

0.0

4

0.0

2

-0.2

3

1.0

0

13

-0.1

0

-0.1

2

-0.0

8

-0.0

6

0.3

5

0.0

9

-0.0

7

0.1

4

-0.0

1

-0.0

4

-0.1

6

0.1

1

1.0

0

14

-0.0

2

-0.0

7

-0.2

8

-0.2

1

0.1

5

-0.0

5

-0.2

8

0.2

2

0.1

2

0.0

0

-0.1

0

0.0

6

0.0

0

1.0

0

Annex 10.1

Annex 10: Drivers of internationalization – regression results


Std. Wsr. SIZE 0.653348 0.207212 3.153042 0.0022

Std. Wsr. TDA2 -0.038096 0.133106 -0.286209 0.7754

Std. Wsr. IED2 -0.083358 0.074100 -1.124950 0.2639

Std. Wsr. PROD2 -0.169367 0.050622 -3.345682 0.0012

Std. Wsr. ΔTNI2 -0.306064 0.079510 -3.849388 0.0002

Std. Wsr. IFS2 -0.030985 0.078787 -0.393270 0.6951

Std. Wsr. DDS2 x IFS2 0.069114 0.069128 0.999810 0.3203

Std. Wsr. ΔTNI2_IND -0.072248 0.034593 -2.088518 0.0398

Std. Wsr. ΔTNI2_LOC 0.020555 0.023195 0.886175 0.3781

Std. Wsr. WSE -0.035613 0.092167 -0.386395 0.7002

Std. Wsr. AGE -0.904176 0.311094 -2.906441 0.0047

Std. Wsr. ITA 0.075991 0.132190 0.574861 0.5669

Std. Wsr. CRIS -0.076368 0.018500 -4.127870 0.0001

Periods included 6



R-squared 0.911975







Mean dependent var -0.070752






Annex 10.2


Std. Wsr. SIZE 0.365723 0.143106 2.555610 0.0124

Std. Wsr. TDA2 0.054611 0.112872 0.483826 0.6298

Std. Wsr. IED2 -0.083034 0.061661 -1.346607 0.1818

Std. Wsr. PROD2 -0.314610 0.029622 -10.62084 0.0000

Std. Wsr. ΔTNI2ep -0.451630 0.062529 -7.222670 0.0000

Std. Wsr. IFS2ep -0.018975 0.067668 -0.280408 0.7799

Std. Wsr. DDS2ep x IFS2ep 0.145469 0.041584 3.498227 0.0008

Std. Wsr. ΔTNI2ep_IND 0.013682 0.038176 0.358398 0.7210

Std. Wsr. ΔTNI2ep_LOC -0.003670 0.021534 -0.170445 0.8651

Std. Wsr. WSE -0.096602 0.070177 -1.376551 0.1724

Std. Wsr. AGE 0.686107 0.265437 2.584822 0.0115

Std. Wsr. ITA 0.003628 0.044433 0.081641 0.9351

Std. Wsr. CRIS -0.006904 0.028902 -0.238871 0.8118

Periods included 6



R-squared 0.913195







Mean dependent var -0.083371






drivers and effects of internationalization in the european

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