Analysis of the mix of profiles within the boards of ...

UNIVERSITEIT GENT

FACULTEIT ECONOMIE EN BEDRIJFSKUNDE

ACADEMIEJAAR 2015 – 2016

Analysis of the mix of profiles within the

boards of listed companies: A European

equation

Masterproef voorgedragen tot het bekomen van de graad van

Master of Science in de

Toegepaste Economische Wetenschappen: Handelsingenieur

Pieter Holbrouck

onder leiding van

Prof. Abigail Levrau

I

PERMISSION

Ondergetekende verklaart dat de inhoud van deze masterproef mag geraadpleegd en/of gereproduceerd worden, mits bronvermelding. Pieter Holbrouck

II

NEDERLANDSTALIGE SAMENVATTING

Deze thesis situeert zich in het kader van corporate governance. Door de schandalen in het begin van

het millenium, de financiële crisis en de steeds meer dynamische omgeving waarin bedrijven zich

bevinden, neemt de nood aan diversiteit binnen de raad van bestuur toe. Het doel van deze thesis is

dan ook een analyse van de mix aan profielen binnen de raad van bestuur in een Europese setting.

Het eerste grote luik bestaat uit twee delen: corporate governance en diversiteit. Er bestaan

verschillende definities van corporate governance en deze thesis focust op de ruimere definities die

ook andere stakeholders buiten de aandeelhouders beschouwen. De verschillende modellen, functies,

wetgevingen en diversiteitsvariabelen betreffende de raad van bestuur worden besproken en

vergeleken voor België, Frankrijk, Spanje en het Verenigd Koninkrijk. Deze thesis splitst diversiteit op

in ‘variëteit’ (variety), ‘scheiding’ (separation) en ‘ongelijkheid’ (disparity) (Harrison&Klein, 2007) en

legt de focus op de voordelen die diversiteit, als variëteit, biedt. De conclusie is dat er in de literatuur

geen concensus is betreffende de impact van diversiteit op de raad van bestuur. Bovendien, diversiteit

heeft zowel voor- als nadelen. De voordelen zijn betere beslissingsvorming en betere toegang tot

middelen, de nadelen zijn tragere beslissingsvorming en verminderde slagvaardigheid.

Het tweede luik is de empirische studie. Gegevens voor 1982 leden van de raad van bestuur werden

verzameld die allen in de BEL 20, CAC 40, IBEX 35 of FTSE 100 zetelden eind 2014. De toegevoegde

waarde van deze thesis ligt bij het toevoegen van functionele achtergronden en studies van de leden

van de raad van bestuur aan de analyse, alsook de grootte van de steekproef. Gezien de descriptieve

aard van het onderzoek en de veelheid aan kruistabellen is het helaas onmogelijk om alle resultaten

hier reeds te vermelden. Voor de overige resultaten verwijs ik naar het empirische luik en de conclusie.

Zowel de corporate governance codes als de resultaten in dit onderzoek wijzen op een grote

meerderheid van niet-uitvoerende bestuurders, wat de ‘agency theory’ ondersteunt, en de

‘stewardship theory’ tegenspreekt. België en het Verenigd Koninkrijk begrijpen het belang van

internationale profielen binnen het bestuur. Frankrijk doet het vooral goed op vlak van

genderdiversiteit en heeft in mindere mate internationale profielen binnen de raad van bestuur.

Spanje lijkt vast te houden aan het ‘old boys’ netwerk’, wat vrouwen ervan weerhoudt in de raad van

bestuur te zetelen. Financiële profielen zijn het meest abundant. De financiële sector heeft een

schrikwekkend lage waarde voor de Blau’s index in vergelijking met andere industrieën: dit wijst op

weinig diversiteit in functionele achtergronden. Voor de resultaten omtrent de andere industrieën

verwijs ik naar het empirische luik en de conclusie.

III

FOREWORD

When writing this dissertation, the final result of two years of hard work, I received support from many

angles and therefore want to express my gratitude to some people.

Firstly, I want to thank Prof. Abigail Levrau for giving me the opportunity to write this dissertation in

the field of corporate governance. Her expertise in the field of corporate governance was invaluable

to me. I want to thank her for the support, feedback and guidance I received, but most of all I want to

say thank you for constantly motivating me and believing in me.

Secondly I want to thank Annelies De Wilde, Research Associate at GUBERNA and dr. Olivier Van der

Brempt, Doctoral Associate at GUBERNA for allowing me to bore them with my questions. Thank you

for the valuable opinions.

I also want to thank my partner Emiel for constantly supporting me and encouraging me. Lastly, I want

to thank my parents, sister Sarah and brother Jan for the encouragement and support I continuously

received during the writing of this thesis, and the five years of studying preceding it.

Pieter Holbrouck

5 May 2016

IV

CONTENTS

PERMISSION ............................................................................................................................................. I

NEDERLANDSTALIGE SAMENVATTING .................................................................................................... II

FOREWORD ............................................................................................................................................ III

CONTENTS .............................................................................................................................................. IV

LIST OF ABBREVIATIONS ........................................................................................................................ VII

LIST OF TABLES ....................................................................................................................................... IX

1 Introduction ................................................................................................................................. 1

2 Literature study ........................................................................................................................... 3

2.1 Corporate governance ......................................................................................................... 3

2.1.1 Introduction ................................................................................................................. 3

2.1.2 Definition ..................................................................................................................... 3

2.1.3 Importance .................................................................................................................. 4

2.1.4 The Board of directors ................................................................................................. 5

2.1.4.1 Classification of directors ........................................................................................ 5

2.1.4.2 One-tier board model .............................................................................................. 6

2.1.4.3 Two-tier board model .............................................................................................. 6

2.1.4.4 The roles of the board of directors .......................................................................... 7

2.1.4.4.1 The monitoring role ........................................................................................... 7

2.1.4.4.1.1 Agency theory ............................................................................................ 8

2.1.4.4.2 The strategic role ............................................................................................... 8

2.1.4.4.2.1 Stewardship theory .................................................................................... 9

2.1.4.4.3 Service role ........................................................................................................ 9

2.1.4.4.3.1 Resource dependency theory .................................................................... 9

2.1.4.4.4 Coordinating role ............................................................................................. 10

2.1.4.4.4.1 Stakeholder theory ................................................................................... 10

2.1.5 Laws, regulations and guidelines. .............................................................................. 10

2.1.5.1 International Level ................................................................................................. 11

2.1.5.1.1 Sarbanes Oxley ................................................................................................ 11

2.1.5.1.2 OECD Principles of Corporate Governance ..................................................... 11

2.1.5.2 National Level ........................................................................................................ 12

2.1.5.2.1 Belgium ............................................................................................................ 12

2.1.5.2.1.1 Board characteristics concerning diversity: Belgium ............................... 12

2.1.5.2.2 France .............................................................................................................. 13

2.1.5.2.2.1 Board characteristics concerning diversity: France .................................. 13

V

2.1.5.2.3 Spain ................................................................................................................ 14

2.1.5.2.3.1 Board characteristics concerning diversity: Spain .................................... 14

2.1.5.2.4 United Kingdom ............................................................................................... 15

2.1.5.2.4.1 Board characteristics concerning diversity: United Kingdom .................. 15

2.2 Diversity ............................................................................................................................. 16

2.2.1 Agency theory ............................................................................................................ 19

2.2.2 Stewardship theory ................................................................................................... 19

2.2.3 Resource dependency theory .................................................................................... 19

2.2.4 Stakeholder theory .................................................................................................... 20

2.2.5 Diversity as variety within the board ........................................................................ 20

2.2.5.1 Educational background and functional background ............................................ 22

2.2.5.1.1 Introduction ..................................................................................................... 22

2.2.5.1.2 Functional background .................................................................................... 22

2.2.5.1.3 Educational Background .................................................................................. 24

2.2.6 Gender diversity ........................................................................................................ 24

2.2.7 National or non-national ........................................................................................... 25

3 Methodology ............................................................................................................................. 26

3.1 Sample ............................................................................................................................... 26

3.2 Data Collection .................................................................................................................. 27

3.2.1 Company .................................................................................................................... 27

3.2.2 Board director ........................................................................................................... 27

3.3 Data classification .............................................................................................................. 28

3.3.1 Industry classification ................................................................................................ 28

3.3.2 Functional background classification ........................................................................ 29

3.3.3 Educational background classification ...................................................................... 30

3.4 Blau’s Index........................................................................................................................ 31

4 Empirical Research .................................................................................................................... 32

4.1 Results ............................................................................................................................... 32

4.1.1 Board size .................................................................................................................. 32

4.1.2 Gender ....................................................................................................................... 32

4.1.2.1 Gender geographically ........................................................................................... 32

4.1.2.2 Gender across Industries ....................................................................................... 34

4.1.2.3 Gender across educational backgrounds .............................................................. 34

4.1.2.4 Gender across functional backgrounds ................................................................. 35

4.1.2.5 Gender across (non-) executive, independent or not, and chairmen ................... 36

4.1.3 Age ............................................................................................................................. 39

VI

4.1.3.1 Age geographically ................................................................................................ 39

4.1.3.2 Age across gender ................................................................................................. 40

4.1.3.3 Age across industries ............................................................................................. 40

4.1.3.4 Age across educational backgrounds .................................................................... 41

4.1.3.5 Age across functional backgrounds ....................................................................... 42

4.1.3.6 Age across (non-) executive, independent or not, and chairmen ......................... 44

4.1.4 Nationality ................................................................................................................. 45

4.1.4.1 Nationality geographically ..................................................................................... 45

4.1.4.2 Nationality across industries ................................................................................. 46

4.1.4.3 Nationality across educational background .......................................................... 47

4.1.4.4 Nationality across functional background ............................................................. 48

4.1.4.5 Nationality across executive, non-executive, independent and chairmen

geographically ....................................................................................................................... 49

4.1.5 Executive or non-executive ....................................................................................... 52

4.1.5.1 (Non-) executive geographically ............................................................................ 52

4.1.5.2 (Non-) executive across industries ........................................................................ 54

4.1.6 Independent or not ................................................................................................... 54

4.1.6.1 Independent or not independent geographically ................................................. 54

4.1.6.2 Independent or not independent across industries .............................................. 56

4.1.7 Educational Background ............................................................................................ 57

4.1.8 Functional Background .............................................................................................. 58

5 Conclusion ................................................................................................................................. 64

5.1 Limitations ......................................................................................................................... 72

5.2 Further research ................................................................................................................ 72

REFERENCES ........................................................................................................................................... XI

Appendix 2.1 ......................................................................................................................................... XVI

Appendix 3.2 ....................................................................................................................................... XXIV

Appendix 3.3 ........................................................................................................................................ XXV

VII

LIST OF ABBREVIATIONS

AF

AFEP/MEDEF

AS

Accommodation and food service industry

Association Française des Entreprises Privées / Mouvement des Entreprises de France

Administrative and support service industry

CEO Chief Executive Officer

CFO Chief Financial Officer

CNMV

CRE

Comisión Nacional del Mercado de Valores

Construction and real estate

CUBG Código Unificado de Buen Gobierno

EBA

EGSW

European Banking Authority

Electricity, gas, steam, and water supply

EU

FI

HTM

European Union

Financial and insurance industry

High-tech manufacturing industry

IC

ICAEW

IFC

IT

Information and communication industry

Institute of Chartered Accountants in England and Wales

International Finance Corporation

Information Technology

LSC

LTM

Ley de Sociedades de Capital

Low-tech manufacturing

MBA

MQ

Master in Business Administration

Mining and quarrying industry

NACE

NEDs

Statistical classification of economic activities in the European Community

Non-Executive Directors

OECD

PAD

PPE

PST

Organisation for Economic Co-operation and Development

Public administration and defence

Philosophy, Politics and Economics

Professional, scientific and technical activities industry

SMEs

St. Dev

Small and Medium-sized Enterprises

Standard Deviation

TMT

TS

Top Management Team

Transporting and storage industry

UK United Kingdom

VIII

US United States

USA United States of America

VBO

WR

Verbond van Belgische Ondernemingen

Wholesale and retail trade industry

IX

LIST OF TABLES

Table 1: Industry classification categories ........................................................................................... 29

Table 2: Functional background or track record classification categories ......................................... 29

Table 3: Educational background classification categories ................................................................. 30

Table 4: Board size across countries .................................................................................................... 32

Table 5: Diversity variable gender geographically............................................................................... 33

Table 6: p-values for gender proportion, independent two samples t-test between countries ....... 33

Table 7: Diversity variable gender across industries ........................................................................... 34

Table 8: Diversity variable gender across educational backgrounds .................................................. 35

Table 9: Diversity variable gender across functional backgrounds .................................................... 36

Table 10: Proportions of women across variable (non-) executive directors geographically ............ 36

Table 11: p-values for gender proportion among executive directors, independent two samples t-

test between countries ......................................................................................................................... 37

Table 12: p-values for gender proportion among non- executive directors, independent two

samples t-test between countries ........................................................................................................ 37

Table 13: Proportions of women across variable independent geographically ................................. 38

Table 14: p-values for gender proportion among independent directors, independent two samples

t-test between countries ...................................................................................................................... 38

Table 15: Diversity variable gender across chairmen geographically ................................................. 39

Table 16: p-values for gender proportion among chairwomen, independent two samples t-test

between countries ................................................................................................................................ 39

Table 17: Diversity variable age geographically .................................................................................. 40

Table 18: Diversity variable age across industries ............................................................................... 41

Table 19: Two-tailed independent samples t-test between several industries ................................. 41

Table 20: Diversity variable age across educational background ....................................................... 42

Table 21: Two-tailed independent samples t-test between several educational backgrounds ........ 42

Table 22: Diversity variable age across functional background .......................................................... 43

Table 23: Two-tailed independent samples t-test between several functional backgrounds ........... 43

Table 24: Diversity variable age across variable (non-) executive directors geographically ............. 44

Table 25: Diversity variable age across variable (non-) independent directors geographically ........ 44

Table 26: Diversity variable age across variable chairman geographically ........................................ 45

Table 27: Diversity variable nationality across countries.................................................................... 45

Table 28: p-values for nationality proportion, independent two samples t-test between countries46

Table 29: Diversity variable nationality across industries ................................................................... 47

Table 30: Diversity variable nationality across educational backgrounds ......................................... 48

Table 31: Diversity variable nationality across functional backgrounds ............................................ 49

Table 32: Diversity variable non-national across variables (non-) executive directors and

independent geographically ................................................................................................................. 50

Table 33: p-values for nationality proportion across executive directors: independent two samples

t-test between countries ...................................................................................................................... 50

Table 34: p-values for nationality proportion across non-executive directors: independent two


Table 35: p-values for nationality proportion across independent directors: independent two


Table 36: p-values for nationality proportion across chairmen: independent two samples t-test


Table 37: Diversity variable (non-)executive geographically .............................................................. 53

X

Table 38: p-values for non-executive proportions among countries: independent two samples t-test


Table 39: Diversity variable (non-)executive across industries........................................................... 54

Table 40: Diversity variable independent (or not) geographically ..................................................... 55

Table 41: p-values for independent proportions among countries: independent two samples t-test


Table 42: Diversity variable independent (or not) across industries .................................................. 56

Table 43: Diversity variable educational background ......................................................................... 57

Table 44: Diversity variable functional background ............................................................................ 59

Table 45: Functional backgrounds represented across industries ...................................................... 63

1

1 Introduction

Corporate governance plays an important role as a safeguard for preventing malpractices, although it

did not prevent the accounting frauds in the beginning of the century (Enron, Ahold and Parmalat).

Neither did corporate governance prevent the financial crisis. Due to these unfortunate happenings,

corporate governance has continued to gain interest (Heidrick & Struggles, 2014).

There is growing interest for the boards to be more diverse (ICAEW, 2014), as “the board is responsible

for the standing of their company in the community” (Levrau & Van den Berghe, 2004, p.462). Although

literature has extensively discussed board diversity, literature has yielded ambiguous results towards

the impact of diversity on board performance. Harrison & Klein (2007) believe the problem lays in

defining diversity too broadly. I will therefore continue to build on the distinction of diversity the

authors make into variety, separation and disparity with the aim of producing a more clear view on the

mix of profiles sitting on the board.

Several corporate governance codes mention the importance of the mix of qualities the board should

consist of, though very little research has actually been done on the qualities or backgrounds

concerning these directors. The added value of this dissertation definitely stems from the large sample

and from including the variables functional background and educational background to board

literature’s widely discussed diversity variables such as gender, (non-) executive, independent, age,

board size, etc. As this dissertation scrutinizes board diversity, it is clear that this dissertation is situated

in the field of corporate governance.

The dissertation consists of several parts: literature study (2), methodology (3), empirical study (4) and

conclusion (5). The literature study consists of two large parts: corporate governance and diversity.

The first part of the literature study gives different views on corporate governance, as it is a widely

discussed topic and can be viewed from different angles. The focus of this dissertation is on definitions

that include other stakeholders than stockholders, as the company nowadays is active in a dynamic

world (Heidrick & Struggles, 2014). The board’s models, roles and members will be extensively

discussed with the aim of understanding the diversity challenge the board faces. Several board

diversity variables will be addressed, as well as quota for these variables, described by soft law, hard

law or corporate governance codes.

2

The second part of the literature study concerns diversity. Board diversity has yielded ambiguous

results on performance, which is why this dissertation continues to build on the three dimensions

Harrison & Klein (2007) believe diversity covers. The focus will be on variety. Much research has

already been done on board diversity, especially concerning gender diversity, but few research has

investigated the actual mix of profiles concerning educational or functional backgrounds of the board.

When discussing functional background the link is made to TMT literature, although it must be argued

that the board is different from an organizational team (Levrau & Van den Berghe, 2007).

The third part, the methodology, describes the setting of the research, how the sample was chosen

and how the data was collected and analysed. It is followed by the empirical study, the fourth part.

Banks were excluded from the sample because many regulations already determine the mix of profiles

within their boards. A lot of attention is given to the categories that were chosen to classify companies

and directors. Several cross-tabulations are performed on the database which I personally built and

results are given. Part five ends with the conclusion, limitations and opportunities for further research.

It must be clear to the reader that this dissertation tries to answer the question “What is the mix of

profiles within the boards?” and the reader should thus note that this dissertation does not try to

measure the impact board diversity has on performance. The aim of this dissertation, however, is to

make a cartography of the profiles within the board of directors within their environment, of top-listed

companies within Belgium, France, Spain and the UK. This dissertation tries to provide a more clear

view on who exactly sits on the boards of listed companies in a European context. Because few

research has been done on functional and educational backgrounds of board directors, both aspects

of diversity as variety, this research is rather exploratory towards diversity as variety, making a

cartography, rather than hypothesis testing. Again, the added value of this dissertation clearly stems

from including the diversity variables functional and educational background to ‘the mix’ when

performing several cross-tabulations. Nonetheless, many board diversity variables are taken into

account as well to determine the mix of profiles on the board.

Finally, I hope that including the functional backgrounds and educational backgrounds of board

members can, in practice, contribute to a deeper understanding of the mix of profiles sitting on these

boards, and contribute to the understanding of board diversity, rather than making results more

ambiguous. The next step is to link these findings to board performance.

3

2 Literature study

2.1 Corporate governance

2.1.1 Introduction

As corporate governance is a very broad topic this dissertation will start with some definitions on

corporate governance and situate them within existing theories. Next the importance of corporate

governance will be highlighted, followed by an elaboration on the board of directors, its models and

its roles. This chapter ends with regulations, guidelines and codes on corporate governance and the

board of directors and its diversity variables.

2.1.2 Definition

Several definitions of corporate governance exist. A first, most basic and traditional, definition of

corporate governance is the following: corporate governance deals with problems resulting from

separating ownership and control (Berle & Means, 1933). Because of the separation between

ownership and control, managers could pursue their own goals at the expense of the shareholders.

This definition can be situated in the agency theory, which is concerned with the agency relationship

between an agent and a principal: one the one hand there is the management that controls the

company (agent) and on the other hand there are the shareholders who own the company (principal).

“Corporate governance involves a set of relationships between a company’s management, its board,

its shareholders and other stakeholders. Corporate governance also provides the structure through

which the objectives of the company are set, and the means of attaining those objectives and

monitoring performance are determined” (OECD Principles of Corporate Governance, 2004, p.11). This

broader definition, in contrast to the previous traditional definition, also includes other stakeholders

than the management and shareholders. The board should not only take other stakeholders into

account, but also let them participate in the board, as Hung (1998) said: corporations are more likely

to respond to the interests of society as a whole, instead of only to the interests of shareholders, by

incorporating the participation of stakeholders in the governing boards. It seems more appropriate to

use this wide definition, also including other stakeholders, looking back at the financial crisis.

Other authors that agree on this contrast concerning the traditional focus on the agency theory and

acknowledge the importance of other stakeholders are Heidrick & Struggles (2014). They say that

nowadays the board is also active in dynamic governance and has to be connected with society raising

the importance of all stakeholders. The authors argue that corporate governance should anticipate to

4

what is happening out there in the business world and should embrace change and create flexibility in

this dynamic world. Once again, quite the opposite of what was happening during the financial crisis

when some signs were clear, however no actions were taken until it was already too late (Kirkpatrick,

2009).

Schleifer and Vishny (1997, p.737) focus on a more juridical point of view on corporate governance,

and pay special attention to legal protection of the investors and how they prevent managers from

using their investments for their own purposes: “Corporate governance deals with the ways in which

suppliers of finance to corporations assure themselves of getting a return on their investment.”

In contrast to the latter, more juridical point of view, Heidrick & Struggles (2014, p.2) point out the

following: “It is important that corporate governance does not become a one size fits all compliance

exercise”. They put forward the view that corporate governance should not me regarded as an act of

compliance that has to be fulfilled but as engagement.

Though all aforementioned definitions on corporate governance are relevant, this dissertation will

predominantly focus on those definitions that are concerned with diversity, and thus include

stakeholders.

2.1.3 Importance

At the beginning of the century several accounting frauds were discovered. The energy company Enron

went bankrupt in 2001 and the worldom scandal was discovered in 2002. But the scandals were not

limited to America. On February 27th of 2003 The Economist called the Ahold scandal ‘Europe’s Enron’

and blamed corporate governance. In September 2003 The Guardian published an article named

‘Parmalat is Europe’s Enron’ and once more shares dropped as a result of malpractice. Corporate

governance was once more to be blamed because of poor performance: it did not prevent certain

malpractices (Kirkpatrick, 2009).

The financial crisis of 2007-2008 is another example of failure of corporate governance. Accounting

standards and regulatory requirements still proved to be insufficient (Kirkpatrick, 2009). During the

financial crisis enormous risks were taken but were poorly controlled, though the OECD Principles

make risk management a clear duty of the board (Kirkpatrick, 2009). Corporate governance had failed

once again.

5

Due to all these malpractices and the events during the financial crisis, corporate governance has

continued to gain interest: regulations, standards and initiatives rose (Heidrick & Struggles, 2014) as

an attempt to make companies more accountable for their actions, improve access to capital and make

them operate more efficiently. Corporate governance continues trying to be a safeguard to prevent

mismanagement (IFC, 2013), and there is a growing interest for the boards to be more diverse (ICAEW,

2014).

2.1.4 The Board of directors

The board of directors is a body of members who govern the firm and who have ultimate decision-

making authority. From an agency theory perspective the board is an internal control mechanism that

copes with divergences in interests between managers and shareholders and make them converge

(Walsh & Seward, 1990). As the board of directors is essential to most definitions of corporate

governance (Maassen, 2002), it is important to know the different models. Literature distinguishes two

different models: the one-tier board model and the two-tier board model.

In Belgium the one-tier board model is the standard. By contrast, in credit institutions and insurance

companies the dual board system is obligatory because the management requires autonomy to serve

the public interest (Davies, Hopt, Nowak & Solinge, 2013). In France, companies can adopt both the

one-tier or two-tier model, although the one-tier model is the model on which French company law

was based. This model uses the board of directors (conseil d’administration). The dual board model

consists of a supervisory board (conseil de surveillance) and managing board (directoire) (Davies, Hopt,

Nowak & Solinge, 2013). In Spain, the one-tier system is adopted. The two-tier model is only used in

European public companies incorporated in Spain (Davies, Hopt, Nowak & Solinge, 2013). In the UK,

companies opt for the one-tier system.

2.1.4.1 Classification of directors

There are three sorts of directors: executive, non-executive and independent directors. These

classifications can be treated as diversity variables within the board: the first variable being executive

or non-executive, and the second variable being independent or not.

The first, executive director, has more in-depth knowledge. He is valuable to the board because he

knows the company and its operations (Davies, 1999). Many non-executive directors join the board

after having been an executive director, to further fulfil the strategic role (see 2.1.4.4.2).

6

The second, non- executive director, sits on the board because he has wide expertise and experience

to contribute. Furthermore the non-executive directors can monitor the performance of the executive

directors and management (Davies, 1999).

Thirdly, directors can be independent. An independent director is always a non-executive director.

Definitions on ‘independent’ vary yet converge towards a stricter meaning of the word (Hopt & Leyens,

2004). In general, independent directors are supposed not to have any relationship with the

corporation. The several existing country-specific corporate governance codes elaborately explain

when a board member can be named independent.

Some countries add a fourth diversity variable: directors who represent employees. In this dissertation,

this only concerns France. Employees can be represented by one or more employee representatives,

if the by-laws provide this option, as written in the AFEP/MEDEF code.

Over the last few years, the proportion of (non-executive) independent directors is gaining more

importance. The focus used to be on the mix of executive and non-executive directors. To date, the UK

code of corporate governance doesn’t even mention anything about the mix of (non-)executive

directors anymore (Davies, Hopt, Nowak & Solinge, 2013).

The board also has to appoint an audit, remuneration, and nomination committee, for which the

corporate governance codes also provides guidelines, but this dissertation will not elaborate further

on the specific committees.

2.1.4.2 One-tier board model

The one-tier board model or unitary board system is adopted in the UK, Belgium, Spain, Canada and

the US (non-exhaustive). In this model both executive- and non-executive directors can sit on the

board. This means that the board is entrusted with both day-to-day business and control of the

business. Because both executive and non-executive directors can sit on the one-tier board, ‘(non-)

executive’ will be a variable of diversity within this model.

2.1.4.3 Two-tier board model

In other countries, Germany and the Netherlands for example, the two-tier board system or dual board

system is adopted. This model distinguishes two layers: the supervisory board and the management

board.

7

The supervisory board is the upper layer of the board and is composed of non-executives only. The

main legal functions of this board are the appointment, supervision and removal of members of the

management board. Although executives are not allowed to have a seat on the supervisory board,

former executives are allowed to have a seat on the supervisory board and many companies try to

make use of former business knowledge by offering former managers seats on the board (Hopt &

Leyens, 2004).

The management board, which is the lower layer, is composed of executive managing directors

(Maassen, 2002). Directors sitting on the management board have executive functions by definition.

However this doesn’t necessarily mean that they are employees of the company (Davies, Hopt, Nowak

& Solinge, 2013). Internal control is an instrument of the management board from which the

supervisory board is excluded (Hopt & Leyens, 2004).

It must be said that one-tier systems try to solve the lack of a two-tier system. They strive for more

transparent division of tasks and allocation of responsibilities through a majority of independent

directors and at least a majority of non-executive directors in the committees (remuneration,

nomination and audit committees). This may compensate for the disadvantages of the unitary board

system (Davies, Hopt, Nowak & Solinge, 2013).

2.1.4.4 The roles of the board of directors

This paragraph discusses the roles of the board. It starts with discussing the three main roles that we

typically find in board literature: monitoring role, strategic role and service role (Zahra & Pearce, 1989),

followed by an elaboration on the coordinating role as described by Hung (1998).

2.1.4.4.1 The monitoring role

The monitoring role or controlling role is based on the agency theory and thus on the fact that the

board has to govern the relationship between the owners and management of the company. The board

has to protect the interests of shareholders and make sure that the management does not act in their

own interest and solve problems resulting from separation from ownership and control (Berle &

Means, 1933). Translated into a diversity variable, the (independent) non-executive directors can

monitor this performance and good behaviour of the executive directors (Brennan & McDermott,

2004). According to Zahra & Pierce (1989) the board has the following duties: selecting and replacing

the CEO, monitoring the CEO’s performance and also evaluating the company’s performance. In Demb

8

& Neubauer (1990) their research corporate directors often commented that even the mere existence

of a board and the obligation to report, results in better performance.

2.1.4.4.1.1 Agency theory

Opening up corporate ownership to the public through share ownership is what caused the separation

of ownership and control. Because of this separation of ownership, incentives exist for managers to

act in their own interests and not necessarily in the interest of the owners (Solomon, 2007). It is

important to control these agency problems when the managers, making important decisions, are not

the major claimants and therefore do not benefit in the wealth of those decisions (Fama & Jensen,

1983). It is up to the board to deal with these divergent interests and attempt to reduce the manager’s

opportunism (Hung, 1998).

2.1.4.4.2 The strategic role

Demb & Neubauer (1990, p.157) highlight the importance of involvement of the board in corporate

strategy: “Primarily through involvement in corporate strategy, boards can play a forward-looking role,

adding value by utilizing its breadth of experience”. Goodstein, Gautam & Boeker (1994, p.242) put

forward that the strategic role of the board “involves taking important decisions on strategic change

that help the organization adapt to important environmental changes”. The authors also argue that

critical periods of environmental turbulence or declines in performance provide opportunities for

initiation of strategic change.

However literature shows no consensus on how active the board of directors should be in strategy.

Zahra (1990) distinguishes three different schools. The first school argues the board should not be

actively involved in strategy and should merely act as the ‘rubber stamp’. According to the second

school the board should be active in strategy review: they should be involved in formulating and

implementing strategies, although they should not develop new strategies. Thus they may recommend

making changes and refinements in company strategies but not develop them themselves. Actually

proposing, initiating and developing strategic changes are part of the third school which goes far

beyond the control function of the board. The strategic role finds its rationale in the stewardship

theory.

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2.1.4.4.2.1 Stewardship theory

As opposed to the agency theory, the stewardship theory rejects the role of the board to cope with

conflicts resulting from separation from ownership and management. On the contrary, it believes the

board to serve as a strategic device that can serve the company through active involvement and

expertise (Levrau & Van Den Berghe, 2007). Thus, the stewardship theory does not recognize

differences in interest between management and ownership and assumes that managers actually want

to act in best interest of the company (Hung, 1998). Translated into board diversity, executive directors

thus act in the best interest of the company, and non-executive directors and/or independent directors

thus attribute their expertise and knowledge to that of the executive directors to help strategic

decision-making and to increase the quality of decision-making (Muth & Donaldson, 1998).

2.1.4.4.3 Service role

According to Zahra & Pearce (1989, p.292) “the service role involves enhancing the company’s

reputation, establishing contacts with the external environment and giving counsel to executives.”

Mintzberg (1983) distinguishes another role: co-opting external influencers. The service role, which

concerns establishing contacts with the external environment, can be compared to Hung’s (1998)

linking role. When a board member sits on the board of two different organizations these organizations

are related by an ‘interlock’. These interlocks are very important to tap into pools of resources (Hung,

1998). The service role finds its rationale in the resource dependence theory.

2.1.4.4.3.1 Resource dependency theory

The resource dependence theory was first described by Pfeffer & Salancik (1978): external resources

can affect the behaviour of an organization in a way that they depend on the providers of these

resources. Organizations depend on their environment because they are not self-sufficient.

Furthermore companies try to be independent of other companies while making other companies

dependent on them. The four benefits or resources that Pfeffer and Salancik (1978) suggest that

directors of the board bring to the organization are advice and counsel, access to channels of

information flow, preferential access to resources and legitimacy. These benefits have found a lot of

support (Hillman, Withers & Collins, 2009) in literature. Hillman, Withers & Collins (2009) their research

even shows that empirical evidence suggests that the resource dependence theory is more successful

for understanding boards than the traditionally used agency theory, though the resource dependence

theory is less often used.

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Concerning diversity within the board, the focus of this dissertation, one of the tasks of the board of

directors in order to optimize the organizations performance is, to act as an essential link between the

organization and resources (Van der Walt & Ingley, 2003). A more diverse board will have a broader

network and a broader pool of resources to tap into. The sort of directors that plays the most important

role in the resource dependency theory are the independent directors, as they have no relationship

with the company and create links with the external environment of the company.

2.1.4.4.4 Coordinating role

“The company, whether it wishes or not, is held accountable for its actions by numerous and varied

groups of stakeholders, and the public” (Demb & Neubauer, 1990, p.158). The coordinating role is

situated in the stakeholder theory and will be scrutinized in the next paragraph.

2.1.4.4.4.1 Stakeholder theory

The stakeholder theory is situated in sociology and is used to describe interactions between

organizations and their environments (stakeholders). The underlying argument is that the organisation

is not only responsible to the stockholders, but also to many other groups such as customers, banks,

environmentalists, etc. According to this theory the organization will fulfil its coordinating role by

negotiating and compromising with stakeholders in their own interest (Hung, 1998). From this

perspective “corporations are seen as superordinate entities in which a variety of parties have vested

legitimate interests” (Maassen, 2002, p.13): the board must thus reflect its stakeholders in order to

protect its stakeholder’s interests.

I have opted to add the coordinating role and stake holder theory because they address diversity within

the board and are therefore important to this dissertation. In order for the board of directors to be

responsible to the stakeholders, the adequate information to make decisions regarding these

stakeholders should be present during the board meetings: a diverse board of directors will dispose of

diverse information and thus be able to make decisions that reflect stakeholders’ interests.

2.1.5 Laws, regulations and guidelines.

In this paragraph we will take a closer look at existing laws, regulations and guidelines concerning

corporate governance on two levels: international and national. Corporate governance seems to be

continuously gaining importance and thus several laws, regulations and guidelines are volatile and

under continuous revision.

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It is important to first explain the difference between soft law and hard law. While hard law arises from

treaties or regulations, soft law lacks obligation or uniformity and is not formally binding. While soft

law is argued not to have any effect, hard law on the contrary is argued to be too difficult to change

and too fixed (Trubek, Cottrell & Nance, 2005). Although soft law is not formally binding it is assumed

to have this effect in the long term and is thus potentially binding (Hillgenberg, 1999). A balance

between soft law and hard law can be more flexible to adapt to the constantly changing environment

of a company and maybe be more effective. This is why the European Union governance codes are

backed by the “comply-or-explain” approach, the trademark in the EU (CNMV, 2015). The “comply-or-

explain” means that companies can deviate from governance codes if they explain why they deviate

from this code and tries to create flexibility.

2.1.5.1 International Level

2.1.5.1.1 Sarbanes Oxley

The Sarbanes-Oxley Act does not apply to the examined countries (Belgium, France, Spain, and the

United Kingdom), but has played a major role in corporate governance in the USA. This Act came into

force in 2002 and is mandatory for all organizations, changing regulations concerning corporate

governance: “ALL organizations, large and small, MUST comply” (Sarbanes-Oxley Act, 2002). This act

mainly consists of compliance of several acts and is rather strict. The act strengthened audit

committees (independence requirements1) and also strengthened disclosure requirements.

2.1.5.1.2 OECD Principles of Corporate Governance

The OECD Principles of Corporate Governance are widely accepted by both OECD and non-OECD

countries, and are non-binding (soft law). They are meant as a reference point. The following definition

is given by the OECD (2004, p.11): “The principles are intended to assist OECD and non-OECD

governments in their efforts to evaluate and improve legal, institutional and regulatory framework for

corporate governance in their countries, and to provide guidance and suggestions for stock exchanges,

investors, corporations and other parties that have a role in the process of developing good corporate

governance. The Principles focus on publicly traded companies, both financial and non-financial”.

Furthermore the Principles of Corporate Governance (2004) acknowledge corporate governance as “a

key element in improving efficiency and growth as well as enhancing investor confidence.” The latter

1 Public law 107-204 – July 30, 2002, Sec. 301. An act to protect investors by improving the accuracy and reliability disclosures made pursuant to the securities laws, and for other purposes.

12

has without doubt gained importance due to the financial crisis. Concerning board diversity, the

G20/OECD Principles of Corporate Governance (2015) mention that groupthink should be avoided and

the board should possess the right mix of backgrounds and competences.

2.1.5.2 National Level

In what follows we will zoom in on the different laws, regulations and corporate governance codes

within the countries included in the sample of this dissertation. For each country we will also address

some board characteristics concerning diversity variables.

2.1.5.2.1 Belgium

Corporate governance in Belgium has two main legal sources: the Belgian Companies Code and the

corporate governance code 2009 (Davies, Hopt, Nowak & Solinge, 2013).

The Belgian corporate governance code, the only reference for listed companies in Belgium by Royal

Decree of 6 June 20102, is the 2009 Code and complements the Code on Belgian law. It is an update on

its predecessor, referred to as the 2004 Code, and is based on voluntary compliance recommendations.

In contrast to the hard law the Sarbanes Oxley Act consists of, the 2009 Code is more flexible, which is

a must in the continuously changing business world (The 2009 Belgian Code on Corporate Governance,

2009). It must be said that though the nature of the 2009 Code mainly is compliance, the BEL-20

companies are very compliant at applying this code according to a study from GUBERNA and VBO

(2011). The comply-or-explain rule is also legally binding since the Royal Decree of 6 June 20103.

2.1.5.2.1.1 Board characteristics concerning diversity: Belgium

The 2009 Code consists of several principles, provisions and guidelines to ensure good corporate

governance. Concerning diversity the code contains a few guidelines stating that the board should be

both small enough to make decisions efficiently and large enough to have a diverse mix of knowledge

and experience. Several quota are given: for example, at least 50% of board directors should be non-

executive directors and at least three of them should be independent, though none of these quota

legally binding. Since the approval of the Belgian law of 6th April 20104 listed companies are obliged by

2 Decreet 6 Juni 2010, Koninklijk besluit houdende aanduiding van de na te leven Code inzake deugdelijk bestuur door genoteerde vennootschappen. 3 Decreet 6 Juni 2010, Koninklijk besluit houdende aanduiding van de na te leven Code inzake deugdelijk bestuur door genoteerde vennootschappen. 4 Wet 6 April 2010, Wet tot versterking van het deugdelijk bestuur bij de genoteerde vennootschappen en de autonome overheidsbedrijven en tot wijziging van de regeling inzake het beroepsverbod in de bank- en financiële sector.

13

law to include a corporate government statement in their annual report. The Belgian law of 28th July

20115 states that at least one third of the board members of government-held companies (by 2012),

publicly listed companies (by 2017) and listed SMEs (by 2019) has to be of a different sex than the

other board members (European Commission, 2013). The 2009 code also contains a guideline stating

that when composing the committee, the board should consider the qualities needed to function

optimally. According to the Spencer Stuart’s Belgium Board Index (2014) the average board size is 12.3

and the BEL 20 executive and non-executive officers have an average age of 55.5 and 58.5 respectively.

2.1.5.2.2 France

The first principles for corporate governance where published in the Vienot report in 1995. AFEP

(Association Française des Entreprises Privées) and MEDEF (Mouvement des Entreprises de France)

prepared recommendations in the years that followed. This set of recommendations constitutes de

AFEP/MEDEF Code, and has been written for unitary boards: adjustments must be made for companies

adopting the two-tier board model. Most of the listed companies in France apply the APEF/MEDEF

code (Davies, Hopt, Nowak & Solinge, 2013) and it was last revised in June 2013. The AFEP/MEDEF

code is not binding and is of the comply-or-explain principle.

Furthermore French law allows the board to choose whether the same or different persons should

hold the offices of CEO and Chairman. In good governance it is considered best to have different

persons assuming these offices and it is considered more respectful because there is a stricter division

of the managing and supervising functions (Davies, Hopt, Nowak & Solinge, 2013). In practice, we see

that more than 50% of the CAC 40 companies had elected a single person to assume both role of the

chairman and CEO in recent years (Davies, Hopt, Nowak & Solinge, 2013).

2.1.5.2.2.1 Board characteristics concerning diversity: France

The French Law of 27 January 2011 6 states that by 2017 women should represent not less than 40%

of board of directors. Regarding independent directors there are no legal obligations, although the

Vienot Report (1995) recommends a minimum number of independent directors on the board. The

AFEP/MEDEF code recommends that half (resp. at least one third) of the directors should be

independent in organizations without (resp. with) controlling shareholders. No recommendations are

5 Wet 28 July 2011, Wet tot wijziging van de wet van 21 maart 1991 betreffende de hervorming van sommige economische overheidsbedrijven, het Wetboek van vennootschappen en de wet van 19 april 2002 tot rationalisering van de werking en het beheer van de Nationale Loterij teneinde te garanderen dat vrouwen zitting hebben in de raad van bestuur van de autonome overheidsbedrijven, de genoteerde vennootschappen en de Nationale Loterij. 6 Loi n°2011-103 du janvier 2011 relative à la representation équilibrée des femmes et des hommes au sein des conseils d’administration et de surveillance et à l’égalité profesionnelle.

14

made for non-executive directors, although the recommendations for independent directors implicitly

are recommendations for non-executive directors as well, as an independent director is per definition

non-executive. The AFEP/MEDEF code also allows for up to 5 employee representatives, elected by the

company’s employees, and advises not to take employee representatives or employee shareholder

representatives into account for these calculations. Furthermore the one-tier board should have

between three and eighteen directors and the managing board a maximum of eighteen. The number

of directors on the boards of the CAC 40 companies ranges from twelve to fourteen. (Davies, Hopt,

Nowak & Solinge, 2013). According to the Spencer Stuart France Board Index 2014, the average board

contains 14 board directors.

2.1.5.2.3 Spain

Spain strictly separates hard from soft law. On the one hand it has the binding provisions such as the

Spanish Company Law, and on the other hand it has the corporate governance recommendations

(CNMV, 2015). Starting from 1997 three good governance codes have been approved in Spain. The

first was the Olivencia Code. All companies rapidly declared their compliance with this code, as it was

not specific enough. The Aldama code was published in 2003 because of these doubtfully sincere

declarations. The Aldama code made an annual corporate governance report mandatory for listed

companies and initiated the comply-or-explain principle (Davies, Hopt, Nowak & Solinge, 2013). In May

2006 the Unified Good Governance Code of Listed Companies was approved by the CNMV (Comisión

Nacional del Mercado de Valores). It has been adapted in 2013 due to new legislation. On 18 February

2015 the Good Governance Code (The Good Governance Code of Listed Companies) was updated again

(CNMV, 2015). This governance code is also subject to the comply-or-explain approach.

2.1.5.2.3.1 Board characteristics concerning diversity: Spain

Concerning diversity the Good Governance Code (2015) suggests having a large majority of non-

executive directors. Furthermore it suggests that at least half of the board consists of independent

directors. A recommendation for the presence of women on the board is made: by 2020 at least 30%

of board members should be women. In Spain’s Boletín Oficial del Estado (2014), which consists of

hard law, it is stated that the annual report should explain which measures are taken towards equal

representation of gender within the board, however no specific objectives are given. The LSC (Ley de

Sociedades de Capital) states that the board of directors for limited liability companies should not have

more than twelve members. However, an exception is made for European public companies that have

opted for the two-tier system: they should have between three and seven members (LSC, Article

15

481)7.The good governance code (CUBG, Código Uniforme de Buen Gobierno) recommends the

number of directors within the board to be between five and fifteen. According to the Spencer Stuart

Spain Board Index (2014) the average board consists of 11.4 board members and their average age is

59.

2.1.5.2.4 United Kingdom

British company law does not structure a one-tier or two-tier board: “The lack of interest shown by the

Companies Acts in issues of board structure, composition and function is a long-standing feature of

British Corporate law : these are matters the companies themselves should decide on” (Davies, Hopt ,

Nowak & Solinge , 2013, p.716). In practice we see that UK companies operate with the unitary board

system.

The UK corporate governance code (2014), or the Code, is the most recent version of the first UK

Corporate Governance code that was produced in 1992 by the Cadbury Committee. The Code is flexible

and consists of principles and provisions. The Code is also subject to the comply- or-explain approach.

2.1.5.2.4.1 Board characteristics concerning diversity: United Kingdom

Concerning board diversity, the UK Corporate Governance Code (2014, p. 11) states that “at least half

the board, excluding the chairman, should comprise non-executive directors determined by the board

to be independent. A smaller company should have at least two independent non-executive directors.”

As stated before it does not mention specifics on other than independent directors. Contrary to Spain,

where it is hard law that addresses equal representation of gender, measures taken should be

described, it is the UK Corporate Governance Code (2014) (soft law) that recommends to describe the

board’s policy, implementation and progress on diversity (including gender diversity). Gender diversity

was first mentioned in the Corporate Governance Code of 2010. According to the Spencer Stuart UK

Board Index (2014) the average age of non-executive directors is 59.3 years and the average executive

director is 52.4 years old. The average board size is 10.5 and 71 percent of all directors were non-

executive directors, excluding the chairmen.

It is clear that the corporate governance codes especially focus on a large majority of non-executive

directors and also independent directors, which is in favour of the agency theory and contradicts the

stewardship theory.

7 Ley de Sociedades de Capital, Artículo 481. Composición del consejo de dirección.

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2.2 Diversity

In the previous part of this dissertation, some diversity variables were already addressed: (non-)

executive, independent or not, board size, and gender. First, this chapter addresses the broad concept

of diversity and highlights its importance. After that, diversity as variety within the board, followed by

functional and educational background diversity will be addressed. Some findings concerning the

diversity variables gender and (non-) national will finally also be addressed.

In order to research the diversity of the board of directors it is important to have a full understanding

of this concept. Because diversity is a widely studied and broad topic, I will explain in what follows

more specifically from which angle this dissertation addresses diversity.

It is important to note that a lot of the literature concerning diversity is situated in the field of sociology

and organizational literature. It also is important to recognize that the board of directors differs from

an organizational team in several aspects. Levrau & Van den Berghe (2007) give a nice overview of why

the board is different: partial affiliation of board members, only episodic interactions, limited time of

board members and few information to work with, preponderance of leaders, complex authority

relationships between directors, changing expectations of work, aura of formality in the board and a

large number of members. When drawing conclusions one should thus constantly keep in mind these

differences.

The past few decades, diversity became more popular in the corporate world (Page, 2007) and in

corporate governance. The UK Corporate Governance Code (2014, p.2) for example points out

“constructive debate can be encouraged through having sufficient diversity on the board”. Page (2007)

even claims that in some cases diversity trumps ability. When Page talks about diversity, he stresses

cognitive diversity: different perspectives, heuristics and different categorizations (people have

different ways of parsing the world into piles of similar things). With his Mathematical Theorem he

proves that maximum diversity should be tried to reach in order to make high quality decisions, though

of course this can’t be applied to the board as such a large number of board members would make the

board extremely inefficient and slow in its decision-making. In practice, maximum diversity as

described by Page (2007) can thus never be achieved within the boardroom, as this would imply an

enormously large, theoretically infinite, number of board members.

Several authors disagree with Page. Sunstein & Hastie (2015) for example claim that large groups can

make bad decisions and go gravely wrong because people like to hear commonly known information

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and a group becomes focused more on shared information as the group becomes larger. Thompson

(2014) claims that there are several flaws8 in Page’s Mathematical Theorem and argues that it is not

applicable in the real-world. Harrison & Klein (2007) also argue that Page has based his theory on

several wrong assumptions, and that empirical evidence for performance benefits of diverse groups in

organisations is contradictory.

Though a lot of research has been done on diversity the outcomes of these studies have been

confusing. Literature on diversity shows no consensus on its results regarding performance. Harrison

& Klein claim that these confusing and ambiguous results are due to the several dimensions that

diversity covers: variety, separation, and disparity. This definition of diversity has also been embraced

by other authors (Solanas, Selvam, Navarro & Leiva, 2012):

- Variety or information diversity refers to variety within a group of knowledge,

heuristics and perspectives and should be maximized. This aspect of diversity will be

covered more widely further in this dissertation.

- Separation is described as “the composition of differences in (lateral) position or

opinion among unit members” (Harrison & Klein, 2007, p.1203).

- Disparity is referred to as vertical differences in power or status or socially valued

assets.

The authors claim that these three distinctive types of diversity have their own consequences which

should be studied separately in order to draw clear conclusions on the effect diversity has on

performance and quality of decision-making.

On the one hand I believe there lays truth in what Page claims: in order to make well-thought, high

quality decisions one needs diversity. On the other hand one must consider the board to be different

from a team. For example, corporate governance codes recommend an optimal number of board

members to make the board efficient, which by definition limits maximal diversity. It is also true what

Harrison & Klein and Thompson state: there is no empirical evidence that embracing diversity has

already yielded remarkable results of performance.

In this dissertation I continue to build on the claim that diversity should be scrutinized more closely

and divided in variety, separation, and disparity, as suggested by Harrison & Klein (2007).

8 More information on this flaws can be found on page 1028 in “Does Diversity trumps Ability?”, published in the American Mathematical Society.

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There is ground to believe that that no evidence has been found due to results of different concepts

over diversity overlapping and abolishing one another. Under these circumstances I do believe that if

the board of directors is more diverse in terms of variety higher-quality decisions can be made if

simultaneously the negative outcomes from disparity and separation are minimized, as described by

Harrison & Klein (2007).

This dissertation will thus mainly address diversity as variety. In addition to the latter, I believe that the

chairman also plays an important role in the making of these high-quality decisions. After all the

chairman is leading the board, and it is up to him/her to make sure that a diverse board is able to

function. Although this is a very interesting subject it will not be addressed more thoroughly in this

dissertation.

Diversity covers many aspects and both demographic and non-demographic variables such as gender,

age, race and ethnicity, tenure, functional background, education and marital status, values, attitudes,

conscientiousness, network ties, affect, dress, individual performance, and pay (Harrison & Klein,

2007). There is reason to believe that demographic characteristics evoke prejudices, biases or

stereotypes between individuals (Harrison, Price, Gavin & Florey, 2002) reducing team performance,

however as members of a team spend more time functioning together these variables might become

less important (Harrison, Price, Gavin & Florey, 2002). However, the reader should note that within

the board these variables probably retain their importance as the board only meets occasionally.

Translated to the diversity distinction of Harrison & Klein, this means that visible characteristics as

gender, age, race and ethnicity may play an important role of diversity as separation and disparity.

Once again, separation and disparity are not the main focus in this dissertation and are to be minimized

in order to achieve performance and quality in decision-making (Harrison & Klein, 2007).

This dissertation focusses on the educational content and functional background of the members of

the board, both variables which are regarded mainly as forms of diversity as variety (Harrison & Klein,

2007) and thus contribute to higher quality decision-making. Diversity as variety will be covered in

paragraph 2.2.5. This dissertation further also takes into account the variables (non-) executive and

independent or not as these are important diversity board variables. In what follows, the board’s roles

will be discussed mainly for the diversity (non-) executive and (non-) independent.

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2.2.1 Agency theory

The agency theory is mainly concerned with the board’s monitoring role and argues that (independent)

non-executive directors should be better at monitoring the executive directors (Brennan &

McDermott, 2004). The agency theory believes that a majority of NEDs reduces the influence the

management has on the board (Muth & Donaldson, 1998) and therefore the diversity variable

executive or non-executive plays an important role in the agency theory.

2.2.2 Stewardship theory

The stewardship theory believes in the opposite of what the agency theory believes in, and is in favour

of a majority of executive directors. The executive directors provide the necessary experience and do

not have conflicts of interest with the shareholders of the company (Muth & Donaldson, 1998). It must

be argued that diversity yields both positive and negative outcomes. Though diversity within the board

of directors might lead to high quality decision making it might also slow down the pace at which

decisions are made, which might especially be important for the board’s strategic role, which finds its

rationale in the stewardship theory. The European Commission states in its green paper ‘The EU

corporate governance framework’ (2011) that “more diversity leads to more discussion, more

monitoring and more challenges in the boardroom and potentially results in better decisions although

getting to those decisions may take more time.” Goodstein, Gautam & Boeker (1994) find that diversity

may slow decisive action, especially in critical times, times of environmental turbulence, when strategic

change should be initiated. Therefore, I believe that depending on whether a more diverse board slows

down decision-making or not, it impacts the board’s strategic role differently. Especially in critical

times, diversity may have a negative impact, even if the quality of the decision-making is excellent.

2.2.3 Resource dependency theory

The resource dependency theory is concerned with the board’s service role and believes that the

broader a director’s network is, the broader the pool of resources to tap into will be, as connections

create options (Muth & Donaldson, 1998). In contrast to the previous two theories, the resource

dependency theory is not directly in favour of a majority of executive, non-executive or independent

directors but is in favour of directors with a high level of network connections, network ties.

Nonetheless I do believe that independent directors may have a different, more distant, network than

executive directors. I also believe that non-national directors probably have different networks than

national directors, and thus this may also be an important diversity variable that supports the resource

dependency theory.

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2.2.4 Stakeholder theory

Over the past decades, the definition of a stakeholder changed from “those who have a stake in an

organisation” to “those who are affected by it” (Sternberg, 1997, p.3), enormously increasing the

number of stakeholders a company has. Similarly as for the resource dependency theory, the

stakeholder theory does not favour a majority of a specific sort of director. Nowadays, anyone can be

a stakeholder to the company. I believe that in practice a company will never be able to represent all

of its stakeholders. Nonetheless, with the aim of fulfilling its coordinating role, it should carefully

consider who its stakeholders are.

Lastly, it is important to take into account the consequences of not having diversity within the board,

and the poor decisions and failure that low diversity may cause. Although research shows both

negative and positive results of diversity within a team on team performance, they are still preferred

over non-diverse teams. After all, diversity is a safeguard that prevents bad and poor quality decision-

making (Sunstein & Hastie, 2015). As nowadays those decisions affect many stakeholders, the latter

should not be forgotten.

2.2.5 Diversity as variety within the board

This section of the dissertation will focus on diversity as variety, as defined by Harrison & Klein (2007,

p.1203): “Composition of differences in kind, source, or category relevant knowledge or experience

among unit members; unique or distinctive information”. The authors claim variety to be at its

maximum when it reaches a uniform distribution with an even spread across all the possible categories.

Translated to functional and educational background this means that all possible functional and

educational backgrounds must be represented equally, exhaustive and disjoint.

This might be supported by Sunstein & Hastie (2015) who make an interesting distinction between two

kinds of group members: cognitively central and cognitively peripheral group members. The cognitively

central group members dispose of knowledge that many other group members dispose of as well. The

authors claim that this sort of information is more likely to be shared due to the disproportionate

influence cognitively central group members have in a discussion, though the uniquely held

information (as referred to as variety by Harrison & Klein) that cognitively peripheral group members

dispose of might also be very important. I believe that if we read between the lines we may conclude

that Sunstein & Hastie might also be in favour of equally represented, exhaustive and disjoint

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functional and educational backgrounds as there might be less shared knowledge and there is more

chance for unique or distinctive information to be brought up in discussions.

Let us now apply these findings to the board of directors. The executive directors would presumably

have rather similar knowledge concerning the day-to-day activities of the companies. If we look at the

independent and other non-executive directors this would mean that all of the independent directors

and other non-executive directors can contribute their own unique expertise and there is no overlap

between directors’ knowledge and experience. In reality their expertise, due to common functional

and educational backgrounds, will never be represented equally, exhaustive and disjoint, but it does

mean that the board must strive to select independent and non-executive members with different

functional and educational backgrounds if it wants to be able to use a diverse mix of expertise and thus

contribute to the quality of decisions made. It also suggests, in practice, not to have too many executive

directors on the board as they presumably have similar backgrounds and would not be contributing to

unique information and could possibly from an opposing team.

Previous studies of diversity within the board mostly focused on variables such as age, gender,

executive or non-executive and independent or not independent. Again, in general I claim such

variables to be more responsible for diversity as separation than variables such as functional and

educational background. Separation is maximized as unit members are split equally at opposing ends

(Harrison & Klein, 2007; Harrison & Sin, 2006), and I believe this will occur more easily through

dichotomous variables such as gender, (non-) executive director or (non-) independent director. Of

course the distinction of executive and (independent) non-executive directors remains crucial to the

board to be able to perform its monitoring role, according to the agency theory, and positive results

cannot be ignored. This dissertation only wants to point to the possible increase of diversity as

separation and its negative consequences.

I believe that functional and educational background will have a bigger impact on diversity as variety

within the board, and when maximized, lead to higher quality decision-making. Variables such as

functional background and educational background have several categories and make it less likely for

directors to fall into only two teams in the board of directors (Harrison & Klein, 2007), which lowers

diversity as separation and reduces the negative components of diversity regarding performance of

the board. If the board would consist of mainly two large groups of board members divided over only

two functional backgrounds, than opposing teams could of course be created. Variety within the board

will reach a maximum when every category of functional background and educational background is

equally spread and represented in the board (Harrison & Klein, 2007). On top of that, the more

22

functional or educational backgrounds are represented within the board, the more they represent

their stakeholders. As Patricia Lenkov (2013) said:”A variety of backgrounds can make the company

more adaptable to its ever changing environment and divergent backgrounds mean tackling the same

idea in differing ways.”

2.2.5.1 Educational background and functional background

2.2.5.1.1 Introduction

This part starts with highlighting the importance of diversity as educational and functional background.

The UK Corporate Governance code (2014) states that diversity should not be limited to gender and

race, but also is about differences of approach and experience. This chapter focusses on this

experience. The European Commission also highlights the importance of professional diversity in its

green paper ‘The EU corporate governance framework’ (2011, p.6): “A variety of professional

backgrounds is needed to ensure that the board as a whole understands certain complexities and

impact of the business on stakeholders”, referring to the stakeholder theory. It also states “accurate

assessment of skills and expertise is the single most important factor in selecting new non-executive

board members”. Kosnik (1990) argued that diversity among board member backgrounds can reduce

the chance of narrow-mindedness and stimulate airing different perspectives.

We will discuss some research concerning educational and functional background as these variables

determine partly the diversity of knowledge and skills that the members of the board possess. Very

little research has yet been done on diversity regarded as both educational and functional background,

although some research has been done on those variables separately, mostly on functional

background. Therefore the added value of this dissertation to previous research is its focus on the

functional and educational backgrounds of board members, a topic barely studied before.

2.2.5.1.2 Functional background

Research done on TMT diversity has yielded both positive and negative results. The positive outcomes

of TMT diversity are higher quality decision-making while the negative outcomes are slower decision-

making, personal conflicts and communication breakdowns (Cannella, Park & Lee, 2008). Once again,

I believe the reason that research might have yielded ambiguous results could be due to a wide

definition of the concept, in this case functional diversity. This is why Bunderson & Sutcliffe (2002)

offer a distinction of the concept. They contribute four different forms of functional diversity of which

23

two will be explained in this dissertation: dominant function diversity and intrapersonal functional

diversity.

Using their definition we could describe the dominant function diversity as diversity in the different

functional areas within which directors have spent the greater part of their careers.

Applying the concept of intrapersonal functional diversity to the board members this would be the

extent to which the board members are narrow specialists or broad generalists (wider range of

functions).

In their research dominant function diversity has a negative effect on information sharing and unit

performance, while intrapersonal functional diversity has a positive effect on these factors (Bunderson

& Sutcliffe, 2002, p.875). Canella, Park & Lee (2008) build on this distinction of functional diversity and

add an internal element: colocation of the TMT members (proportion of members who work at the

same location. Introducing this variable the results changed slightly: TMT intrapersonal functional

diversity still has a positive influence on team functioning, but now also dominant functional diversity

can have a positive influence on firm performance if the TMT is collocated (Cannella, Park & Lee, 2008).

Although research has yielded ambiguous results, this might be an interesting distinction to build on.

In contrary to what Harrison & Klein (2007) claim, Cannella, Park & Lee (2008, p.780) suggest

“executives of broad functional experience have the specialized knowledge needed to effectively make

decisions and the skills, networks, and referent power needed to interact effectively on a TMT”, this

means overlapping variety. Note that these executive directors often become non-executive directors.

Gibson & Vermeulen (2003) join Harrison & Klein (2007) on their definition of maximum variety as they

claim that when a team’s demographic heterogeneity is extremely high the group becomes very open

minded and shares all the unique information. When a group is only moderate heterogenic the group

stops serving as one cohort, opinions won’t converge and potential benefits from heterogeneity won’t

be realized. I believe this goes hand in hand with what Canella, Park & Lee found: dominant function

diversity was found to have a positive influence on firm performance when the TMT is collocated,

which I believe made them serve as one cohort. O’Reilly & Williams (1996) found similar results: when

group members are more familiar with one another they are more able to use unique information.

When translating these results to the board, the question arises whether the board serves as a cohort.

Firstly the board is not collocated. Secondly the board only meets occasionally and has limited time

through which it more difficult to be familiar with other board members and serve as one cohort. Also,

the board definitely does not perform well in terms of demographic heterogeneity: most board

24

members are of certain age, many are national, a majority are men, etc. It is clear that possessing

expertise and knowledge is one thing, sharing knowledge is another. Research clearly yields ambiguous

results. There is only one thing all of the previous research in this chapter agrees on: in some way,

diversity adds more information and ways of thinking which can lead to better decision-making.

Regardless of whether variety in backgrounds has a positive or negative impact on board performance,

the diversity variable functional background can be relevant to the board in performing its service and

coordinating role. For example, a board member with a financial background will probably have many

connections in the financing industry. Another example is that if many of the board members of a

company have public backgrounds, the government might be an important stakeholder to the

company or the industry the company operates in.

2.2.5.1.3 Educational Background

Regarding the board, to the best of my knowledge, little research has yet been done on educational

background of the board members. It must be said that board members are of a certain age and

therefore might have a lot more functional background. Therefore I believe that functional background

might be more relevant in some cases. However, educational background might be interesting

regarding the board of director’s network, and educational background is intended to be a starting

point for the functional background. I believe the educational background of a director can mainly

enhance the execution of the service role: finding resources, and attributing knowledge to the board.

2.2.6 Gender diversity

The kind of diversity most referred to within corporate governance literature is gender diversity.

Several measures were taken to increase women representation in boards: some countries have

introduced quota and some have used push initiatives that were driven by social justice motives (Ali,

Ng & Kulik, 2013). Facts and figures of gender diversity increasing, exist already. In October 2013 the

European Commission (2014) found that the European average of women on the boards was 17.8%.

Egon Zehnder (2014)9 found that from 2012 to 2014 the percentage of women directors on the boards

increased from 15.6% to 20.3%. On the other hand Egon Zehnder (2014) also found that regarding the

board chair positions, only 3% were held by women. Although gender diversity is increasing, studies

9 Egon Zehnder’s European Board Diversity Analysis (2014) profiles the boards of more than 350 of the largest companies across 17 European countries.

25

have yielded ambiguous results. Both positive and negative associations have been found between

gender diversity and various performance measures (Ali, Ng & Kulik, 2013).

2.2.7 National or non-national

In their European board diversity analysis Egon Zehnder (2014) found that in recent years the European

boards became especially more diverse in gender diversity, but also in nationality diversity. A director

is considered to be national, if he is a national to the same country where the company has its

headquarters. As stated before, the diversity variable (non-)national might especially be interesting

concerning the company’s service role. An international board member is probable to have a large

external network of unexplored resources available. Egon Zehnder (2014) shows that 32.3% of board

directors are non-nationals.

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3 Methodology

3.1 Sample

This dissertation scrutinizes the board of directors of the top listed companies of Belgium, France,

Spain and the UK. The sample consists all listed companies that were constituents of the associated

stock indexes (BEL 20, CAC 40, IBEX 35 and FTSE 100 respectively) in December 2014 before the last

changes to these indexes were made for 2014. All directors that were a member of one or several of

these boards at December 31th 2014 were included. The reason why these indexes were chosen is

because of the simple fact that each of these indexes mainly consists of companies governed by a

unitary board, and thus the results can easily be compared between countries, with no distinction to

be made between unitary and dual boards. Also, these stock indexes are capitalisation-weighted

indexes and contain companies with the highest capitalisation, and thus represent a lot of the market

capitalisation. The IBEX 35 contains the 35 most liquid Spanish stocks traded in the Madrid Stock

Exchange General Index, but is also capitalisation-weighted.

All companies that were part of the financial and insurance service industry were included, except for

the banks. The EBA (European Banking Authority) guidelines (2012) apply to all board members of

credit institutions, regardless of their board system, and state that board members should be suitable,

meaning they should have the right qualities and theoretical experience. The board member’s

theoretical experience should be assessed, and is supposed to consist of qualifications related to the

areas of economics, law, administration and financial regulation and quantitative methods. Therefore

the theoretical and functional experience (diversity) of board members of banks is to be determined

by these guidelines and are excluded from the sample. The reader should note that for all the following

calculations and results, all banks are excluded and are not included in the sample. Lastly two

companies were excluded because their headquarters is located outside of Europe. All the previous

taken into account, the total sample consists of 164 companies or 1982 directors. A list of included and

excluded companies, with reason of exclusion, can be found in appendix 3.1.

Actually, other samples are used for the empirical study. When drawing conclusions for Europe, all of

the above companies and board members of the above specified total sample are included. When

drawing conclusions for individual countries (Belgium, France, Spain and the United Kingdom), all

companies who have their headquarters in those countries are included. In the empirical study it will

be clearly specified which sample is used based on the words ‘Europe’, ‘Belgium’, ‘France’, ‘Spain’, and

the ‘UK’. Other samples are all companies operating in a specific industry, etc.

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3.2 Data Collection

3.2.1 Company

Several data was collected for the companies regarding several variables. A first variable is the stock

index (or indexes) the company is listed on. Another variable is the company’s headquarters. This

variable is crucial to determine which companies are included in the sample (to draw conclusion on

country-level), as well as to determine whether a director is national or non-national. This information

was easily found on the Internet. Apart from the company’s headquarters, the sector or main

economic activity (determined by largest operational revenue) of the company was collected,

represented by its NACE code. The NACE code applies to all European Union members, but we looked

for the NACE code for all the companies included in the sample. In most cases the NACE code was

clear, in other cases the primary NACE code was that of holding companies (6420) or head activities

(7010)10. In the latter cases, secondary NACE codes were looked for and chosen. The NACE codes were

collected using the Amadeus database and annual reports for 2014, as well as the company’s website.

The NACE code that was chosen is that of the economic activity that adds most value to the company,

as described by the European Commission (2008) in the NACE Rev. 2.

3.2.2 Board director

Regarding the board members of these companies, the following information was gathered: gender,

(non-) executive, (non-) independent, chairman (or not), employee representative (or not), employee

shareholder representative (or not), age, (non-) national, educational background, and functional

background. All this information was obtained using a company’s annual report for 2014 (Belgium and

the UK), registration document for 2014 (France) and annual corporate governance document for 2014

(Spain). On top of that, several Internet sources were consulted: Wikipedia, Bloomberg, company’s

website etc., to enhance the quality of the information. No data was obtained using e-mails or

interviews, as the sample simply was too large to obtain this information for each individual director.

10 A list of NACE codes can be found on http://ec.europa.eu/competition/mergers/cases/index/nace_all.html

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3.3 Data classification

Next the companies were classified according to their main economic activity, represented by their

primary NACE code, henceforth referred to as industry. The board members were classified two times:

firstly, according to their functional background, and secondly according to their educational

background. As data would be comparable, and with the intention of calculating a diversity index

(Blau’s index, more on this further in the dissertation), one dominant educational or functional

background category was chosen for each board member, and they were all assigned to one category

only. To avoid subjectivity of classifying the data, the investigator triangulation method was used. In

case of difficulties, another researcher was asked to classify the data. If both researchers allocated the

subject into to same category it was assigned to this category. If both researchers gave a different

answer it was assigned to the category ‘broad educational background’ or ‘broad functional experience

or indecisive’ as it could be allocated to different categories.

3.3.1 Industry classification

The companies were classified according to their main economic activity, or primary NACE code. In this

dissertation, this code represents the industry the company operates in. With the goal of being able to

draw conclusions from analysing the data, several adjustments were made to this classification.

Firstly, the manufacturing category (‘C’) was split in two subcategories: low-tech manufacturing and

high-tech manufacturing. The low-tech manufacturing category is represented by a combination of the

medium-low technology companies and low technology companies, based on NACE Rev. 2 (codes

10:18, 19, 22:25, 31:33). Secondly the high-tech manufacturing category is a combination of the high-

technology and medium-high technology categories (codes 20:21, 26:30) (European Commission, s.d.).

Secondly, the real estate (‘L’) and construction (‘F’) industries were joined in one category: real estate

and construction. Many companies were operating in both sectors and this unified category enhances

the interpretation of results.

Thirdly, categories ‘D’ (Electricity, gas, steam and air conditioning supply) and ‘E’ (Water supply;

sewerage; waste management and remediation activities) are joined in one category: electricity, gas,

steam, and water supply. Both categories are part of utilities and are highly regulated industries. The

categories were joined because the water supply industry only consisted of two companies.

29

Lastly, some categories weren’t included as there weren’t any companies that had this economic

activity as their main activity. This led to categories, given in table 1:

Table 1: Industry classification categories

Mining and quarrying Accommodation and food service activities

Manufacturing high tech Information and communication

Manufacturing low tech Financial and insurance activities

Electricity, gas, steam, and water supply Professional, scientific and technical activities

Construction and real estate Administrative and support service activities

Wholesale and retail trade Public administration and defence

Transporting and storage

3.3.2 Functional background classification

The functional background variable defined in this dissertation is, in fact, a combination of a director’s

professional experience and track record. Therefore the classification categories include both

professional functions as industry experience. I believe the combination of both (function and

experience) in one variable contributes to the ability to choose a dominant ‘functional background’.

For example, if a director held several CFO functions throughout his/her career in several industries,

the category ‘finance’ was chosen. If, however, a director held several different functions, always in

the energy industry, he/she was allocated to the category ‘energy’. The categories were defined

looking at the data and were chosen trying to achieve maximal workability for analysing. This lead to

the categories given in table 2:

Table 2: Functional background or track record classification categories

Broad or indecisive professional background Marketing, sales & communication

Energy Medicine

Finance Public services

Human resources Real estate

Industrial & technology Retail

IT Telecommunications

Juridical & legal Other

30

The category ‘broad or indecisive professional background’ was chosen when the investigator

triangulation led to different outcomes, because the director had multiple and equally important

functional backgrounds. This category represents the ‘broad generalist’ in intrapersonal functional

diversity as described by Bunderson & Sutcliffe (2002). If a director was allocated to another category,

he is assumed to be a narrow specialist. Employee representatives and employee shareholder

representatives, sitting on the board of CAC 40 companies, were assigned to the category ‘other’, as

their backgrounds actually represent knowledge and experience on a lower level. In very few of these

cases, these representatives held executive positions, and were then assigned the same way as the

other directors. The category ‘other’ contains backgrounds that had little occurrences. In appendix 3.2,

a list of examples for each category is included.

3.3.3 Educational background classification

Last but not least, directors were classified according to their educational background. In most cases,

this was straightforward. In a minority of cases however it wasn’t as clear and several rules were

established and thus need to be taken into consideration. Firstly, many directors obtained several

degrees. More importance was given to the bachelor and initial education, and in case of doubt or

many degrees in different fields the category ‘broad education’ was chosen for the director. This

category represents, similar to the distinction made for intrapersonal functional diversity by

Bunderson & Sutclifffe (2002), a broad generalist in intrapersonal educational diversity. All other

categories are specialist categories. Secondly, it important is that an MBA was not considered in the

educational background as most directors obtained this during their career. Because of the high

profiles of the directors of the board, this was not considered as adding up to diversity and therefore

is not included. Thirdly, many directors qualified as a chartered accountant during their careers, and

this was included in their functional background, as it mostly led to a financial profile. The classification

was made looking at the data to obtain maximal workability, and is given in table 3. A list of categories

and elements is given in appendix 3.3.

Table 3: Educational background classification categories

Broad education Mathematics & IT

Economics Medicine

Engineering Political and social sciences

History, languages & philosophy Sciences

Law Other

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3.4 Blau’s Index

Concerning the measuring of diversity, the question of ‘How to measure diversity?’ arises. This

dissertation will follow the suggestion of using an index according to its type of diversity (Harrison &

Klein, 2007; Solanas, Selvam, Navarro & Leiva, 2012). The Blau’s Index will only be calculated for the

board members’ functional and educational background, variables both defined as diversity as variety.

Applied to variety, Blau’s Index has been used in previous studies (Solanas, Selvam, Navarro & Leiva,

2012), and will therefore also be used in this study. The Blau’s index (Blau, 1977) is calculated as

follows:

𝐵 = 1 − ∑ 𝑝𝑖2

𝑘

𝑖=1

p = proportion of group members in the ith category

k = number of categories

The higher the value for B is, the higher the dispersion of members over the several categories, with a

maximum value of (k -1)/ k. This value can only be reached if n = mk (m being a positive integer, n being

group size). In this research the upper value is never reached, and thus there is no problem. The lower

bound for B is zero. The values for the Blau’s index cannot be compared between attributes of interest

when they contain a different number of categories, unless normalised (divided by ‘n’, group size)

(Blau, 1977). In the empirical study, the normalised Blau’s index will be used to compare between

groups, and is used as a proxy for how diverse (diversity as variety) a board is.

32

4 Empirical Research

4.1 Results

4.1.1 Board size

The average board size found in the results is compared to numbers found in the Spencer Stuart Board

Indexes 2014(for Belgium, France, Spain, and the UK), given in table 4 below. Differences result from

the companies that are included in the sample. Also, in this research banks were excluded from the

sample. For most companies we found similar results, whereas for Spain we found a higher board size.

The Spencer Stuart Spain Board Index (2014) includes 92 companies (apart from 34 companies listed

on the IBEX 35), which thus have a larger board size than companies listed on the IBEX 35. Whereas

France has the largest board size, the UK has the smallest board size.

Table 4: Board size across countries

Belgium France Spain UK

Results 12.3 14.6 13.25 10.9

Index 12.3 14 11.4 10.5

Table with numbers concerning board size across countries, calculated using SPSS.

4.1.2 Gender

4.1.2.1 Gender geographically

In our sample, 23.5% directors are women. Table 5 shows that France performs above average, with

30.9% of the board directors being women. This finding is significantly different compared to all other

countries and Europe on a 5% significance level, given in table 6 which shows the p-values for a two-

tailed independent two samples t-test. On the contrary Spain performs worse, with a proportion of

only 17% being female directors, this number is also significantly different from all other proportions

found in table 5 for α = 0.05. Belgium and the UK perform very similarly to the average for Europe. The

p-values show that the proportion of women do not differ significantly for the following combinations

on a significance level of α = 0.05: Belgium and Europe, the UK and Europe, and the UK and Belgium.

33

Table 5: Diversity variable gender geographically

Europe Belgium France Spain UK

# % # % # % # % # %

Man 1517 76.5 161 77.0 273 69.1 264 83.0 697 76.8

Woman 465 23.5 48 23.0 122 30.9 54 17.0 210 23.2

Total 1982 100.0 209 100.0 395 100.0 318 100.0 907 100.0

Table with numbers concerning the diversity variable gender across countries and Europe, calculated using SPSS.

Table 6: p-values for gender proportion, independent two samples t-test between countries

p- values Europe Belgium France Spain UK

Europe

Belgium 0.436

France < 0.001 0.020

Spain 0.005 0.044 < 0.001

UK 0.430 0.475 0.012 0.010

* Two-tailed independent two sample t-test between the gender proportions in between countries/Europe

In Belgium, France and the UK all board of directors, included in the sample, contained women. In

Spain, three out of twenty-four (12.5%) companies did not even have one woman on the board. The

Good Governance Code (2015) suggests that by 2020 the companies should have 30% women on the

board. A binomial test shows that indeed Spain’s gender proportion significantly (α = 0.05, p< 0.001)

differs from the 30% objective. It is clear that Spain still has to gap a bridge. Regardless of whether

Spain will meet this criteria by 2020, it is worrying that three companies have not even got one woman

sitting on its board. Belgium and France are close to their objectives, 33% (law of 28th July 2011)11 and

40% (French law January 2011)12 women by 2017 respectively. The objective for the FTSE 100

companies for the UK is 25% by 2015. Binomial tests show that on a 5% significance level only for the

UK the proportion of women found in the results do not differ from the objectives set. This objective

was indeed achieved (Hutchison, 2015). Two-tailed p-values for Belgium, France, and the UK are 0.001,

< 0.001, and 0.105 respectively.

11 Wet 28 July 2011, Wet tot wijziging van de wet van 21 maart 1991 betreffende de hervorming van sommige economische overheidsbedrijven, het Wetboek van vennootschappen en de wet van 19 april 2002 tot rationalisering van de werking en het beheer van de Nationale Loterij teneinde te garanderen dat vrouwen zitting hebben in de raad van bestuur van de autonome overheidsbedrijven, de genoteerde vennootschappen en de Nationale Loterij 12 Loi n°2011-103 du janvier 2011 relative à la representation équilibrée des femmes et des hommes au sein des conseils d’administration et de surveillance et à l’égalité profesionnelle.

34

4.1.2.2 Gender across Industries

For Europe, some gender proportion differences were found among different industries, with the

mining and quarrying industry having the largest proportion of men on the boards (80%). The

accommodation and food service activities sector has the lowest proportion of men on the boards

(67.4%), as shown in table 7. The public administration and defence industry had 83.3% men on the

board, but was excluded since it only represents one company with twelve directors. When comparing

the gender proportions for industries to the average gender proportion for Europe, using a two-tailed

independent two sample t-test, no significant differences are found. When performing the same test

between the accommodation and food service activities and mining and quarrying industry, we do find

significant results (p = 0.039 , α = 0.05) and conclude that the gender proportion differs between these

two industries.

Table 7: Diversity variable gender across industries

Table with numbers concerning the diversity variable gender across industries for Europe, calculated using SPSS. * A two-tailed independent two sample t-test between the gender proportions for industries and European average.

4.1.2.3 Gender across educational backgrounds

We can also look at gender across educational backgrounds, given in table 8. All educational

background profiles have a majority of men, but the engineering profile on the board is clearly

dominated by men: 89.3% of engineering profiles are men, this is significantly different from the

European average on a significance level of 5%. The political and social sciences (52.3%), mathematics

& IT (56.0%), and history, languages & philosophy profiles (57.4%) have the highest proportions of

women, and are also significantly different from the European average on a significance level of 5%. P-

values were calculated using a two-tailed two samples t-test, testing whether the proportion found for

gender is significantly different on a 5% significance level than the overall gender average found for

the total sample (76.5 % of man).

Industry / Gender Men Women Total p-value*

Accommodation and foods service 67.4% 32.6% 46 0.076

Administrative and support service 79.4% 20.6% 34 0.346

Construction and Real Estate 78.9% 21.1% 190 0.227

Electricity, gas, steam & water supply 74.1% 25.9% 185 0.231

Financial and insurance 78.9% 21.1% 237 0.204

Information and communication 78.1% 21.9% 187 0.311

Manufacturing High Tech 73.4% 26.6% 342 0.107

Manufacturing Low Tech 77.6% 22.4% 245 0.350

Mining and quarrying 80% 20% 140 0.172

Professional, scientific and technical 78.9% 21.1% 57 0.337

Transporting and storage 75.0% 25.0% 72 0.384

Wholesale and retail trade 75.3% 24.7% 235 0.341

35

Table 8: Diversity variable gender across educational backgrounds

Table with numbers concerning the diversity variable gender across educational backgrounds across Europe, calculated using SPSS. * Absolute number of directors allocated to the corresponding category, excluding missing values. ** Including missing values, as these are missing values. *** Two-tailed two sample t-test. Tested on differences between gender proportions between two samples: educational background category and gender proportion for Europe.

4.1.2.4 Gender across functional backgrounds

The proportion of women on the boards according to their functional backgrounds is another way of

scrutinizing them. Table 9 shows that directors with financial, industrial & technology, retail and real

estate backgrounds are often men: in more than 80% cases directors with these backgrounds were

men, which is significantly different than the European average proportion of men found in the total

sample on a 5% significance level. The opposite was found for directors with energy, human resources,

juridical & legal, IT, and marketing, sales & communication backgrounds: they have lower percentages

of women compared the European average. Two-tailed p-values show that the percentages for these

profiles are indeed significantly different on a 5% significance level. Remarkable is that all of the

defined functional background categories are represented by a large majority of men, except for

human resources. Three quarters of directors with a narrow specialist human resources background

are women. The human resources profile is unique in this regard. This finding is significant on a 5%

significance level, and thus human resources profile has a significantly (α = 0.05) different proportion

of women compared to the European average.

Educational background /

Gender

Men Women Total* p-value***

Broad education 66.7% 33.3% 201 0.001

Economics 78.6% 21.4% 585 0.145

Engineering 89.3% 10.7% 319 < 0.001

History, languages & philosophy 57.4% 42.6% 61 < 0.001

Law 72.9% 27.1% 203 0.126

Mathematics & IT 56.0% 44.0% 50 < 0.001

Medicine 68.2% 31.8% 22 0.181

Political and social sciences 52.3% 47.7% 44 < 0.001

Sciences 73.6% 26.4% 106 0.245

Other 53.1% 46.9% 32 0.001

Missing** 81.9% 18.1% 359 0.012

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Table 9: Diversity variable gender across functional backgrounds

Table with numbers concerning the diversity variable gender across functional backgrounds across Europe, calculated using SPSS. * Absolute number of directors allocated to the corresponding category, excluding missing values. ** Including missing values, as these are missing values. *** One-sided two sample t-test. Tested on differences between gender proportions between two samples: functional background category and gender proportion of Europe.

4.1.2.5 Gender across (non-) executive, independent or not, and chairmen

Table 10 shows that in Europe 7.1% of executive directors are women, and 27.3% of non-executive

directors are women. Spain does not even have one single executive female director on the board.

Belgium has the highest proportion of executive women on the board (10% of executives were found

to be women). France has the highest proportion of non-executive female directors (33.4%). Women

are thus more likely to be non-executive directors.

Table 10: Proportions of women across variable (non-) executive directors geographically

Women Europe Belgium France Spain UK

# % # % # % # % # %

Executive 27 7.1 2 10 3 7.7 0 0 21 8.5

Non-executive 438 27.3 46 24.3 119 33.4 54 20.1 189 28.6

Table with numbers concerning the diversity variable gender for (non-)executive directors across countries and Europe, calculated using SPSS.

Functional background / Gender Men Women Total* p-value***

Broad / Indecisive 73.9% 26.1% 207 0.202

Energy 68.6% 31.4% 86 0.046

Finance 81.1% 18.9% 783 0.004

Human Resources 25.0% 75.0% 16 < 0.001

Industrial & Technology 84.9% 15.1% 245 0.002

IT 62.5% 37.5% 56 0.008

Juridical & Legal 63.2% 36.8% 68 0.006

Marketing, sales & communication 58.2% 41.8% 98 < 0.001

Medicine 72.7% 27.3% 11 0.384

Public Services 71.4% 28.6% 98 0.123

Real Estate 91.3% 8.7% 23 0.048

Retail 87.1% 12.9% 70 0.019

Telecommunications 79.4% 20.6% 34 0.346

Other 73.1% 26.9% 130 0.189

Missing** 71.9% 28.1% 57 0.210

37

Table 11 shows whether differences between these countries are significant or not for executive

directors. As could be expected all proportions obtained are significantly different than Spain’s

proportion of zero percent (α = 0.05). Between other countries and Europe, no results are found

significantly different on the 5% significance level.

Table 11: p-values for gender proportion among executive directors, independent two samples t-test between countries


Europe

Belgium 0.313

France 0.445 0.382

Spain 0.026 0.012 0.023

UK 0.260 0.409 0.434 0.0162

Two-sided independent two samples t-test between gender proportions among executive directors across countries and Europe compared to each other.

Table 12 shows the same results as table 11, but for non-executive directors. For non-executive

directors, the gender proportions do not significantly differ between Belgium and Spain on a 5%

significance level, results do differ between Spain and any other country or Europe. For the UK the

proportion of women among non-executive directors only differs with Spain (α= 0.05)

Table 12: p-values for gender proportion among non- executive directors, independent two samples t-test between countries


Europe

Belgium 0.190

France 0.010 0.014

Spain 0.007 0.142 < 0.001

UK 0.265 0.122 0.056 0.004

Two-sided independent two sample t-test between gender proportions among non-executive directors a countries and Europe compared to each other.

38

Table 13 shows that for Europe 32.5% of independent directors are women. France has the highest

proportion (38.4%) of female independent directors, Spain the lowest (25.9%).

Table 13: Proportions of women across variable independent geographically

Women Europe Belgium France Spain UK

# % # % # % # % # %

Independent 364 32.5 32 36.8 83 38.4 36 25.9 184 32.4

Not Independent 101 11.7 16 13.1 39 21.8 18 10.1 26 7.7

Table with numbers concerning the diversity variable gender for (non-)independent directors across countries and Europe, calculated using SPSS.

Table 14 shows whether the proportions given in table 13 are significantly different from each other,

and displays the p-values for their independent two samples t-tests. Spain has a different proportion

of independent women compared Belgium and France, but not compared to the UK and Europe.

France has a different proportion of independent women than the European average. For other

countries, no significant difference is found compared to this European average.

Table 14: p-values for gender proportion among independent directors, independent two samples t-test between countries


Europe

Belgium 0.205

France 0.046 0.397

Spain 0.058 0.041 0.007

UK 0.484 0.208 0.057 0.069

Two-tailed independent two sample t-test between gender proportions among independent directors a countries and Europe compared to each other.

When looking at the number of female chairmen, given in table 15, the findings are very clear. From

all 165 chairmen (164 companies were included, one company with two chairmen) only four are

women. Remarkably, two out of these four women chair on Belgian boards, although only seventeen

of the included countries have their headquarters in Belgium. France on the other hand, has not even

a single woman chairing on a CAC 40 company. Women clearly aren’t at all likely to be chairman, and

mostly are independent non-executive directors.

39

Table 15: Diversity variable gender across chairmen geographically


# % # % # % # % # %

Chairwomen (%) 4 2.4 2 11.8 0 0.0 1 4.2 1 1.2

Total # directors 165 100 17 100.0 27 100.0 24 100.0 83 100.0

Table with numbers concerning the diversity variable gender across chairmen across countries and Europe, calculated using SPSS.

Table 16 shows the whether the proportions given in table 15 differ significantly geographically. As

could be expected when looking at the percentages in table 15, Belgium has significant values when

compared to most countries/Europe (α = 0.05). The results are not significant when comparing to

Spain, probably due to the low number of observations for both countries. None of the other p-values

are significant on a 5% significance level and thus few proportions significantly differ from each other

for α = 0.05.

Table 16: p-values for gender proportion among chairwomen, independent two samples t-test between countries


Europe

Belgium 0.019

France 0.208 0.034

Spain 0.303 0.179 0.141

UK 0.261 0.010 0.284 0.170

Two-tailed independent two sample t-test between gender proportions among chairwomen a countries and Europe compared to each other.

4.1.3 Age

4.1.3.1 Age geographically

Concerning age, both average (with its standard deviation), upper quartile, lower quartile, and median

were calculated. The latter is included, as it is less affected by outliers. Table 17 shows that the average

Spanish board member is the oldest for the four countries included within the sample. One fourth of

Spanish board members is older than 69, and 10% is even older than 72. Table 17 further also shows

that Belgium and the UK have the youngest directors serving on the boards.

40

Table 17: Diversity variable age geographically


Median 59 58 60 63 58

Average 58.8 57.2 59.7 62 57.6

St. Dev 8.5 9.6 9.3 9.4 7.3

Upper quartile 65 64 66 69 63

Lower quartile 53 52 54 56 52

Missing* 5.8% 2.9% 0.3% 20.4% 4.1%

Table with numbers concerning the diversity variable gender, calculated using SPSS * Missing percentage is calculated as the proportion of missing values on the total number of values.

4.1.3.2 Age across gender

When looking at the age of male directors, these do not differ much from the numbers given in table

11. When looking at the age of female directors, we see that the average female European director

(55.7) is younger than her male colleague (59.7) in Europe. Belgian, French, Spanish and UK female

directors are on average 55.2, 55.8, 57.7, and 55.3 respectively, years old. The average age gap

between male and female directors is the largest for France (5.7 years) and Spain (5.1 years), and the

smallest for Belgium (2.6 years) and the UK (2.9 years). The European average age gap between female

and male directors is 4 years.

4.1.3.3 Age across industries

The age of the board members is not only restricted to geographical comparison, but can also be

compared across industries. The mining and quarrying industry, as given in table 18, has the eldest

directors serving on the boards, the same industry that contained the highest proportion of men. Note

that the mining and quarrying industry also had the highest proportion of men sitting on its boards.

The accommodation and food service, and administrative and support service industries have the

youngest board members. Table 19 shows whether there are significant age differences for some

combinations of industries. The mining and quarrying industry has a significant (α =0.05) age difference

compared to the accommodation and food service industry, financial and insurance industry, public

administration and defence and electricity, gas, steam and water supply industry. The independent

two samples t-test requires checking if variances are equal, this was done using the Levene’s test, and

in case equal variances could not be assumed the p-value was corrected.

41

Table 18: Diversity variable age across industries

Table with numbers concerning the diversity variable gender across industries, calculated using SPSS. * Total number of board members included for the calculation, excluding missing values **Only one company

Table 19: Two-tailed independent samples t-test between several industries

Table with numbers concerning age differences between industries, across Europe, calculated using SPSS. * Equal variances are not assumed, and a correction is applied.

4.1.3.4 Age across educational backgrounds

Table 20 shows the variable age across educational backgrounds for the directors. Directors with broad

educational experience are the oldest, together with directors with political and social sciences

educational backgrounds. Directors with engineering, law, and sciences backgrounds are on average

also older than the average European director. The opposite was found for directors with studies in

economics and mathematics & IT. Table 21 below again shows whether differences are significant on

a 5% significance level, and provides the Levene’s test and p-values. When the Levene’s test is

significant, equal variances are not assumed and the p-value must be corrected, however, the test was

Industry / Age Median Average St. Dev Total*

Accommodation and foods service 55 55.8 8.0 46

Administrative and support service 56 56.3 7.0 32

Construction and Real Estate 59 59.3 9.0 171

Electricity, gas, steam & water supply 58 59 8.8 164

Financial and insurance 58 57.9 8.5 231

Information and communication 58 58.9 8.5 177

Manufacturing High Tech 59.5 59.6 8.6 332

Manufacturing Low Tech 59 59 6.6 234

Mining and quarrying 61 61 6.4 136

Professional, scientific and technical 58 58.5 9.4 55

Public administration and defence** 53 55.7 7.2 11

Transporting and storage 57 58.8 8.2 63

Wholesale and retail trade 58 57.8 8.9 216

Total

Industries (Mean age) Levene’s Test p-value Different?

MQ (61) - AF (55.8) 0.044 < 0.001* Yes

MQ (61) - FI (57.9) 0.004 < 0.001* Yes

MQ (61) - PAD (55.7) 0.326 0.011 Yes

MQ (61) - EGSW (59) 0.002 0.026 * Yes

FI (57.9) - AF (55.8) 0.906 0.134 No

CRE (59.3) - AS (56.3) 0.068 0.067 No

HTM (59.6) - TS (57.8) 0.549 0.481 No

42

never significant. Not all combinations are given, but the combinations given doe provide some

significant differences: there is an age difference between broad generalists and economists, broad

generalists and mathematicians or IT, and between engineers and economists.

Table 20: Diversity variable age across educational background

Table with numbers concerning the diversity variable age across educational backgrounds, across Europe, calculated using SPSS. * Absolute number of directors allocated to the corresponding category, excluding missing values. ** Including missing values, as these are missing values.

Table 21: Two-tailed independent samples t-test between several educational backgrounds

Table with numbers concerning differences between educational backgrounds, across Europe, calculated using SPSS.

4.1.3.5 Age across functional backgrounds

Table 22 shows the diversity variable age across the directors’ functional backgrounds. Directors with

industrial & technology profiles and public services profiles are older than the average European

director. In contrast, directors with marketing, sales & communication profiles and retail profiles are

Educational background / Age Average St. Dev Total*

Broad education 60.0 8.9 192

Economics 57.7 8.1 544

Engineering 59.3 8.54 301

History, languages & philosophy 56.8 8.9 57

Law 59.7 8.6 186

Mathematics & IT 55.8 7.5 50

Medicine 58.5 8.1 22

Political and social sciences 60.0 6.4 43

Sciences 59.1 7.0 100

Other 55.1 7.0 31

Missing** 59.9 9.4 359

Educational backgrounds (Mean age) Levene’s Test p-value Different?

Broad education (60.0) - Economics (57.7) 0.194 0.001 Yes

Broad education (60.0) – Mathematics & IT (55.8) 0.371 0.002 Yes

Political and social sciences (60.0) – Economics

(57.7)

0.154 0.074 No

Political and social sciences (60.0) – Engineering

(59.3)

0.287 0.627 No

Engineering (59.3) – Economics (57.7) 0.753 0.007 Yes

Law (59.7) – Engineering (59.3) 0.262 0.644 No

Medicine (58.2) – Broad Education (60.0) 0.200 0.450 No

Political and social sciences (60.0) – History,

languages & philosophy (56.8)

0.057 0.054 No

43

younger than the average European director. Table 23 shows the p-values for independent two-

samples t-tests between several functional backgrounds to prove that there are indeed significant age

differences between industries. The test was not run for every single combination (a total of 91

combinations), but is given between several high and low values to look if these age gaps are indeed

significant. Age differences were found between public services and human resources, public services

and finance, industrial & technology and marketing, sales & communication, juridical & legal and IT,

and between finance and industrial & technology on a 5% significance level.

Table 22: Diversity variable age across functional background

Table with numbers concerning the diversity variable age across functional backgrounds, across Europe, calculated using SPSS. * Absolute number of directors allocated to the corresponding category, excluding missing values. ** Including missing values, as these are missing values.

Table 23: Two-tailed independent samples t-test between several functional backgrounds

Table with numbers concerning the diversity variable age across functional backgrounds, across Europe, calculated using SPSS.

Functional background / Age Average St. Dev Total*

Broad / Indecisive 59.6 8.8 196

Energy 59.2 6.4 80

Finance 58.2 8.1 746

Human Resources 55.7 7.4 12

Industrial & Technology 61.0 8.7 230

IT 57.3 8.1 56

Juridical / Legal 60.7 9.2 59

Marketing, sales &

communication

56.8 8.1 92

Medicine 62.3 6.6 10

Public Services 63.2 7.5 93

Real Estate 58.2 8.0 23

Retail 57.3 7.5 66

Telecommunications 58.5 6.5 32

Other 55.0 7.9 128

Missing** 61.7 14.2 45

Functional backgrounds (Mean age) Levene’s Test p-value Different?

Public services (63.2) - Human Resources (55.7) 0.762 0.001 Yes

Public services (63.2) – Finance (58.2) 0.082 < 0.001 Yes

Industrial & Technology (61.0) – Marketing, sales &

communication (56.8)

0.907 < 0.001 Yes

Juridical & Legal (60.7) – IT (57.3) 0.243 0.036 Yes

Energy (59.2) – Retail (57.3) 0.508 0.088 No

Finance (58.2) – Industrial & Technology (61.0) 0.873 < 0.001 Yes

44

4.1.3.6 Age across (non-) executive, independent or not, and chairmen

When looking at the variable age across executive directors and we compare the age of non-executive

directors, table 24 shows that executive directors have a significantly different average age for Europe,

France, and the UK than their non-executive colleagues. Before performing an independent two

samples t-test, the Levene’s test was again performed to check if the assumption of equal variances

holds, if this wasn’t the case the Levene’s correction was applied. Only for Belgium no significant

difference is found between the age of executive and non-executive directors. Interestingly we find a

very large age difference for the UK.

Table 24: Diversity variable age across variable (non-) executive directors geographically

Age Europe Belgium France Spain UK

Mean executive (Std. Dev) 54.1 (6.71) 55.6 (9.23) 56.7 (6.62) 59.1 (8.28) 52.7 (5.70)

Mean non-executive (St. Dev) 59.9 (8.5) 57 (9.62) 60.1 (9.49) 62.5 (9.56) 59.4 (7.01)

Levene’s Test < 0.001 0.21 0.004 0.443 < 0.001

p-value* < 0.001** 0.423 0.006** 0.032 < 0.001**

Table with numbers concerning the diversity variable age across non-executive directors across Europe and countries, calculated using SPSS. * Independent two samples t-test, two-sided. ** Equal variances not assumed as proven by the Leven’s Test for Equality of Variances.

Table 25 performs the same test as table 24 but for the dependent variable age and factor variable

(non-) independent. Results are similar as for table 24. Independent directors are significantly (α =0.05)

of different age than their colleagues for Europe, France, Spain, and the UK (α =0.05). Only for Belgium

no age difference was found between independent and non-independent directors.

Table 25: Diversity variable age across variable (non-) independent directors geographically


Mean independent (Std. Dev) 60.1 (7.82) 57.7 (9.97) 61.7 (8.48) 63.2 (8.69) 58.8 (6.64)

Mean not independent (St. Dev) 57 ( 9.09) 56.9 (9.3) 57.5 (9.7) 61.0 (9.96) 55.6 (7.93)

Levene’s Test < 0.001 0.771 0.585 0.132 0.001

p-value* < 0.001** 0.598 < 0.001 < 0.064 < 0.001

Table with numbers concerning the diversity variable age across (non-)independent directors across Europe and countries, calculated using SPSS. * Two-sided independent samples -test ** Equal variances not assumed as proven by the Levene’s test for equality of variances.

45

We can also check whether there is an age difference between chairmen and other directors, this is

displayed in table 26. The findings are interesting: no significant difference (α = 0.05) was found for

Belgium, France and Spain two-sided. The most extreme results are found for the UK. The average

chairmen in the UK is 64.8 years old, while the average director who is not chairing the board is 56.8

years old. Chairmen in the UK are significantly (α = 5%) off different age than their colleagues. Similar

results hold for Europe.

Table 26: Diversity variable age across variable chairman geographically


Mean chairmen (Std. Dev) 63.2 (8.28) 58.6 (16.3) 59.3 (5.87) 65.3 (9.21) 64.8 (5.4)

Mean non-chairmen (St. Dev) 58.4 (8.42 57.1 (8.77) 59.8 (9.50) 61.7 (9.42) 56.8 (7.06)

Levene’s Test 0.013 0.207 0.006 0.708 0.002

p-value* < 0.001** 0.525 0.659** 0.087 < 0.001

Table with numbers concerning the diversity variable age across chairmen across Europe and countries, calculated using SPSS. * Two-sided independent samples -test ** Equal variances not assumed as proven by the Levene’s test for equality of variances.

4.1.4 Nationality

4.1.4.1 Nationality geographically

Table 27 shows the proportion of non-national directors serving on the boards of Belgium, France,

Spain, and the UK. The findings clearly illustrate that Belgium and the UK have the highest proportion

of non-nationals serving on their boards. On the contrary, Spain has the lowest proportion of non-

nationals serving its boards. It must be noted that for approximately one third of Spain’s board

members, no nationality was found. The European average is 67.2 % of nationals and thus 32.8%

international board directors.

Table 27: Diversity variable nationality across countries


# % # % # % # % # %

National 1196 67.2 139 68.5 290 76.3 174 82.9 571 67.5

Foreigner 583 32.8 64 31.5 90 23.7 36 17.1 275 32.5

Total* 1982 100.0 203 100.0 379 100.0 210 100.0 846 100.0

Table with numbers concerning the diversity variable nationality across countries, calculated using SPSS * Missing values excluded

46

Table 28 shows the p-values for a two-sided independent two samples t-test between countries and

Europe. Between Belgium and Europe, the UK and Europe and Belgium and the UK no significant

differences were found on a significance level α = 0.05. They all have a similar proportion of

international directors on the board. France and Spain have a significantly (α =0.05) different

proportion of non-national directors on its boards compared to its European average. Differences are

also found on a 5 % significance level between Spain and Belgium, France and Belgium, France and

Spain, the UK and France and lastly Spain and the UK.

Table 28: p-values for nationality proportion, independent two samples t-test between countries


Europe

Belgium 0.353

France < 0.001 0.021

Spain < 0.001 < 0.001 0.031

UK 0.438 0.392 0.001 < 0.001

Two-sided independent two sample t-test between nationality proportions of a countries and Europe compared to each other.

4.1.4.2 Nationality across industries

Another way of comparing the proportion of non-national directors, is across industries, given in table

29. We find interesting results for the mining and quarrying, and the administrative and support

activities industry: as opposed to the other industries included in the sample, these industries contain

a majority of non-national directors. These finding significantly differ from the European average (α=

0.05). The manufacturing industry also has a large number of non-national directors. These results

suggest that the companies that are part of all the above mentioned industries operate in more

international environments, and therefore are more dependent on establishing links with the

company’s external international environment (service role). Table 29 shows that most non-national

proportions significantly differ from the European found average on a 5% significance level. The

construction and real estate and electricity, gas, steam and water supply industry have extremely high

proportions of national directors. These findings significantly differ from the European average for α =

0.05. Now let us compare some of the ‘extreme’ industries to ‘moderate’ industries. When comparing

industry percentages among each other, using a two-sided independent two samples t-test, the mining

and quarrying industry even has a significantly different proportion of non-nationals compared to the

47

high-tech manufacturing industry (p < 0.001, α = 0.05). The construction and real estate industry and

electricity, gas, steam and water supply industry both have significantly different proportions of non-

nationals when compared to the high-tech manufacturing industry. Note that the high-tech

manufacturing industry was used as moderate industry in the calculations. It is clear that the

proportion of non-nationals varies strongly among industries.

Table 29: Diversity variable nationality across industries

Table with numbers concerning the diversity variable nationality across industries, calculated using SPSS * Absolute numbers, excluding missing numbers ** p-value calculated for the two-sided independence two samples t-test, comparison between the proportion of national directors between an industry percentage and the European average (67.2%)

4.1.4.3 Nationality across educational background

Table 30 shows the diversity variable (non-) nationals across educational backgrounds. A two-tailed

independent sample t-test is performed between the proportion of non-nationals of the educational

background categories and the European average (32.8%). Directors with a medical background have

the highest proportion of non-nationals. This finding is significant on a significance level of α =0.05,

and thus the findings are different from the European average. Economics and political and social

sciences also have a higher than average proportion of international directors, this finding is also

significant on a 5% significance level. The lowest proportion of non-national directors is found for

directors with an educational background of mathematics & IT, although this finding is not significant

(α = 0.05).

Industries / Nationality National Non-national Total* p-value**

Accommodation and food service

activities

78.6% 21.4% 42 0.059

Administrative and support activities 46.7% 53.3% 30 0.009

Construction and real estate 89.9% 10.1% 158 < 0.001

Electricity, gas, steam and water supply 88.2% 11.8% 144 < 0.001

Financial and insurance activities 75.6% 24.4% 225 0.005


Manufacturing high tech 54.8% 45.2% 325 < 0.001

Manufacturing low tech 56.3% 43.7% 231 < 0.001

Mining and quarrying 33.6% 66.4% 137 < 0.001

Professional, scientific and technical

activities

70.5% 29.5% 44 0.322



48

Table 30: Diversity variable nationality across educational backgrounds

Table with numbers concerning the diversity variable nationality across educational backgrounds, across Europe, calculated using SPSS. * Absolute number of directors allocated to the corresponding category, excluding missing values. ** Including missing values, as these are missing values. *** Two-tailed two samples t-test. Tested on differences between non-national proportions between two samples: educational background category and non-national proportion for Europe (32.8%, n = 1179).

4.1.4.4 Nationality across functional background

Table 31 shows the results for the cross-tabulation between diversity variables nationality and

functional background. Again, a two-tailed two sample t-test was performed, this time between the

proportions of non-nationals between functional backgrounds and the European average (32.8%).

Medical functional background has, as medical educational background, the highest proportion of non-

national directors, this finding is significantly different on a 5% significance level. IT,

telecommunications, and industrial & technology backgrounds also contain high; significantly (α =

0.05) different proportions of non-nationals compared to the European average. Another interesting

finding is the real estate profile, which consists of 95% national directors, significant for α = 0.05.

Financial and public service profiles are mostly national profiles (28.5% and 23.1% non-national

directors respectively), these numbers are also significantly different than the European average.

Educational background / Nationality Non-nationals Total* p-value***

Broad education 34.1% 179 0.365

Economics 38.8% 523 0.008

Engineering 29.5% 281 0.144

History, languages & philosophy 35.6% 59 0.327

Law 27.5% 178 0.079

Mathematics & IT 24.5% 49 0.112

Medicine 71.4% 21 < 0.001

Political and social sciences 51.2% 41 0.007

Sciences 30.9% 97 0.350

Other 50.0% 30 0.024

Missing** 22.7% 321 < 0.001

49

Table 31: Diversity variable nationality across functional backgrounds

Table with numbers concerning the diversity variable (non-) national across functional backgrounds, calculated using SPSS. * Absolute number of directors allocated to the corresponding category, excluding missing values. ** Including missing values, as these are missing values. *** Two-tailed two samples t-test. Tested on differences between non-national proportions between two samples: functional background category and European average (32.7%, n = 1179).

4.1.4.5 Nationality across executive, non-executive, independent and chairmen

geographically

Table 32 shows the proportions of executive, non-executive and independent directors that are also

non-national directors. We find the highest proportions of non-nationals for executive and

independent directors for Belgium. The UK has the highest proportion of non-nationals among non-

executives compared to Europe and other countries. France and Spain show very low percentages of

non-national directors among executive directors. French boards didn’t contain chairwomen and thus

also no non-national chairwomen. Spain has the lowest proportion of non-national directors across all

categories in table 32. Tables 33, 34, 35 and 36 show which differences are significant for α = 0.05.

Functional background / Nationality Non-National Total* p-value***

Broad / Indecisive 31.0% 187 0.313

Energy 34.6% 81 0.370

Finance 28.5% 717 0.025

Human Resources 27.3% 11 0.349

Industrial & Technology 41.9% 215 0.005

IT 53.2% 47 0.002

Juridical & Legal 29.1% 55 0.284

Marketing, sales & communication 40.4% 89 0.071

Medicine 72.7% 11 0.003

Public Services 23.1% 91 0.028

Real Estate 5.0% 20 0.004

Retail 37.5% 64 0.218

Telecommunications 53.3% 30 0.009

Other 36.8% 117 0.190

Missing** 22.7% 44 0.080

50

Table 32: Diversity variable non-national across variables (non-) executive directors and independent geographically

Non-nationals


Executive % (Absolute number*)

22.7% (77) 30.0% (6) 5.0 % (2) 5.9 % (2) 23.2% (52)

Non-executive % (Absolute number*)

35.2% (506) 31.7% (58) 25.8% (88) 19.3% (34) 35.9% (223)

Independent % (Absolute number*)

39.0% (399) 42.2% (35) 30.8% (66) 21.3% (20) 36.0% (192)

Chairman % (Absolute number*)

21.5% (34) 23.5% (4) 0.0 % (0) 4.5 % (1) 21.5% (17)

Table with numbers concerning the diversity variable nationality spread over ‘executive’, ‘non-executive’, ‘independent’, and ‘chairman’ across Europe and countries, calculated using SPSS. * Excluding missing values.

Table 33 shows the p-values for the nationality proportion differences among executive directors

between countries and Europe. For Belgium and the UK this proportion differs with Spain and France

(significance level = 0.05). Also for both Belgium and the UK no differences are significant with Europe

and themselves (between Belgium and the UK) on a significance level of α = 0.05.

Table 33: p-values for nationality proportion across executive directors: independent two samples t-test between countries


Europe

Belgium 0.226

France 0.005 0.004

Spain 0.011 0.008 0.432

UK 0.445 0.247 0.004 0.010

Two-sided independent two sample t-test between nationality proportions among executive directors of a countries and Europe compared to each other.

Results are similar for the nationality proportion among non-executive directors when performing the

independent two samples t-test between countries and Europe, as shown in table 34. Again there is

no significant (α = 0.05) difference between Belgium and Europe or the UK and Europe, neither is there

a significant (α = 0.05) difference between Belgium and the UK. For Belgium there is no significant

difference with France on a significance level of 5%, but there is again a significant difference with

Spain. Spain has a significant different proportion of non-nationals compared to any other country or

Europe on a 5% significance level. France also has a significantly different proportion on a significance

level of 5% compared to Europe, Spain and the UK.

51

Table 34: p-values for nationality proportion across non-executive directors: independent two samples t-test between countries


Europe

Belgium 0.175

France < 0.001 0.076

Spain < 0.001 0.004 0.049

UK 0.380 0.148 0.001 < 0.001

Two-sided independent two sample t-test between nationality proportions among non-executive directors of a countries and Europe compared to each other.

Table 35 shows the p-values for the non-national proportion across independent directors

geographically, again using a two-tailed two samples t-test. Similar differences are found as for non-

national proportions among variables executive and non-executive: no significant (α = 0.05)

differences are found between Belgium and the UK, Belgium and Europe, and the UK and Europe. All

other proportions differ significantly on a 5% significance level. France and Spain have significantly

different proportions of non-national directors among independent directors, especially Spain shows

strong results compared to other countries.

Table 35: p-values for nationality proportion across independent directors: independent two samples t-test between countries


Europe

Belgium 0.283

France 0.012 0.031

Spain < 0.001 0.001 0.043

UK 0.123 0.138 0.088 0.003

Two-sided independent two sample t-test between nationality proportions among independent directors of a countries and Europe compared to each other.

52

Lastly, table 36 below shows that, performing an independent two-samples t-test for nationality

proportion across chairmen geographically, results are not significantly (α = 0.05) different between

France and Spain. Neither are the results significantly different between Europe, Belgium, and the UK

on the other hand on a significance level of α = 0.05. France and Spain’s proportion of non-national

chairmen is different than for Europe, Belgium, and the UK (α = 0.05).

Table 36: p-values for nationality proportion across chairmen: independent two samples t-test between countries


Europe

Belgium 0.425

France 0.004 0.004

Spain 0.030 .039 0.133

UK 0.500 0.428 0.004 0.033

Two-sided independent two sample t-test between nationality proportions among chairmen a countries and Europe compared to each other.

The average European country has, based on the sample, a proportion of 32.8% of non-national

directors (203 missing values out of a total of 1982 values, 10.2%, were not included). Of the executive

board members, 22.7% were non-nationals, and 35.1% of non-executive directors were non-nationals

(again for both, missing values were not included). These results show that non-executive directors

play a more important international role, and support the resource dependency theory and service

role, as the service role concerns establishing contacts with the external, her international, and

environment. This is even validated more when looking at the proportion of independent non-national

directors, which is 39.1% (chairmen excluded).

4.1.5 Executive or non-executive

4.1.5.1 (Non-) executive geographically

When looking at the diversity variable executive or non-executive, table 37 shows that Belgium has

the highest proportion of non-executives, closely followed by France. The UK is the worst performer

of the four countries included in the sample, with a proportion of 72.9% of non-executives. The average

proportion of non-executives found for Europe is 80.9%.

53

Table 37: Diversity variable (non-)executive geographically


# % # % # % # % # %

Executive 378 19.1 20 9.6 40 10.1 50 15.7 661 27.1

Non-executive

1604 80.9 189 90.4 355 89.9 268 84.3 246 72.9

Total 1982 100.0 209 100.0 395 100.0 318 100.0 907 100.0

Table with numbers concerning the diversity variable (non-)executive across countries and Europe, calculated using SPSS

Table 38 shows whether differences between countries and Europe are significant or not, using an

independent two samples t-test. Proportions of non-executives are not significantly different between

Europe and Spain, and Belgium and France on a 5% significance level. All other results are significantly

different from each other for α 0.05.

Table 38: p-values for non-executive proportions among countries: independent two samples t-test between countries


Europe

Belgium < 0.001

France < 0.001 0.423

Spain 0.074 0.022 0.013

UK < 0.001 < 0.001 < 0.001 < 0.001

Two-sided independent two sample t-test between (non-) executive proportions among countries and Europe compared to each other.

All countries (no objective was found for France) comply with the quota set by their governance codes.

The 2009 Code (Belgium) and the UK corporate governance code both state that at least half of the

board should consist of non-executive directors. When comparing the results found with a norm

(binomial test) of 50%, testing one-sided on α = 0.05, we can conclude that we can reject the null

hypothesis for Belgium and the UK that the proportions are lower than the norm, with p-values both

smaller than 0.001. For Spain also 84.3% fulfils the objective of ‘a large majority’ of non-executive

directors set by the Good Governance Code (2015). When performing a similar test for Spain, using

75% non-executives as ‘large majority’, a one-sided test with this norm results in significant results (p

< 0.001 , α = 0.05) and thus Spain complies with its soft objective.

54

4.1.5.2 (Non-) executive across industries

When looking at the number of executive and non-executive directors across industries, given in table

39, the industrial sectors (manufacturing, mining and quarrying, and construction and real estate) have

the lowest proportion of executive directors (results are only significantly different from the European

average for the high-tech manufacturing industry, for α = 0.05). The transport and storage industry,

and electricity, gas, steam and water supply industry also have a low proportion of executive directors.

The highest proportions are found for the accommodation and food service activities industry,

administrative and support activities industry, and financial and insurance activities industry. All

industries have large majorities of non-executive directors. Results are only significantly different from

the European average for the financial services industry (α = 0.05).

Table 39: Diversity variable (non-)executive across industries

Table with numbers concerning the diversity variable (non-executive), calculated using SPSS * Absolute numbers, excluding missing numbers ** Two-tailed independent two samples t-test between non-executive proportions among industries and the European average

4.1.6 Independent or not

4.1.6.1 Independent or not independent geographically

To calculate statistics concerning independent directors on the board, chairmen were excluded, as

suggested by the UK corporate governance code. This was applied for all companies of all countries in

favour of the comparability of data. Table 40 shows that for Europe, approximately 60% of non-

executive directors are also independent. Belgium and France are close to this average with 57.8% and

58.4% respectively. The 2009 Code for Belgium states the board should contain at least three

Industries Executive Non-executive Total* p-value**


activities

28.3% 71.7% 46 0.059


Construction and real estate 19.5% 80.5% 190 0.447

Electricity, gas, steam and water supply 15.7% 84.3% 185 0.129



Manufacturing high tech 15.2% 84.8% 342 0.043

Manufacturing low tech 15.5% 84.5% 245 0.086

Mining and quarrying 15.7% 84.3% 140 0.160


activities

22.8% 77.2% 57 0.242



55

independent directors. All complied, except for one company. On average, France complies with the

objectives set by the AFEP/MEDEF (50%). 58.4% is significantly different on a 5% significance level using

a binomial test (p = 0.001). The UK has a larger proportion of independent directors (66.4%) and

complies with the proportion of independent non-executives set by the UK corporate governance code

(>=50%). This 66.4% is significantly different from 50% (p < 0.001, α = 0.05). Spain has a lower

proportion of independent directors (46.9%), and on average does comply with the 50% proportion

set by the Good Governance Code (2015): 46.9% is not significantly different from 50% (p = 0.144, α =

0.05)

Table 40: Diversity variable independent (or not) geographically

Europe Belgium France Spain UK # % # % # % # % # %

Independent 1085 59.7 111 57.8 215 58.4 38 46.9 547 66.4 Not

Independent 732 40.3 81 42.2 153 41.6 156 53.1 277 33.6

Total 1817 100.0 192 100.0 368 100.0 294 100.0 824 100.0

Table with numbers concerning the diversity variable independent or not across Europe and countries, calculated using SPSS

Table 41 shows that for Spain the proportion of independent directors is significantly different

compared to Europe, Belgium, France, and the UK (one-sided thus p-values from table 34 are divided

by two for Spain, α = 0.05). For the UK the proportion of independent directors is also significantly

different compared to Europe, Belgium, France, and Spain (again α = 0.05). For Belgium the results are

significantly different compared to Spain and the UK on a 5% significance level.

Table 41: p-values for independent proportions among countries: independent two samples t-test between countries


Europe

Belgium 0.305

France 0.322 0.446

Spain < 0.001 0.009 0.002

UK 0.001 0.012 0.004 < 0.001

Two-sided independent two sample t-test between independent proportions among countries and Europe compared to each other.

56

4.1.6.2 Independent or not independent across industries

When looking at the number of independent directors across industries, given in table 42, the mining

and quarrying industry stands out with a proportion of 73.6% independent directors. This proportion

is significantly differs from the European average (59.7%) on a 5% significance level. This very high

proportion can be partly explained because all but one of the companies that were categorised in the

mining and quarrying industry have their headquarters in the UK, which has the highest proportion of

independent directors. The construction and real estate industry has the lowest proportion of

independent directors of all industries in the sample, and this finding significantly differs from the

European average (α = 0.05). More specifically, Spain has a proportion of 33.7% of independent

directors in the construction and real estate industry (of a total of 83 members). However, this 33.7%

does not differ significantly from the 41.6% found for the construction and real estate industry for

Europe using a two-tailed independent two sample t-test (α =0.05, p = 0.109).

Table 42: Diversity variable independent (or not) across industries

Table with numbers concerning the diversity variable independent or not across industries, calculated using SPSS * Absolute numbers, excluding missing numbers ** Two-tailed independent two samples t-test between independent proportions among industries and the European average

Industries Independent Not Independent Total* p-

value***


activities

54.3% 45.7% 46 0.231


Construction and real estate 41.6% 58.4% 190 < 0.001

Electricity, gas, steam and water supply 53.0% 47.0% 185 0.039



Manufacturing high tech 63.5% 36.5% 342 0.094

Manufacturing low tech 53.9% 46.1% 245 0.042

Mining and quarrying 73.6% 26.4% 140 0.001


activities

63.2% 36.8% 57 0.298



57

4.1.7 Educational Background

When looking at the directors’ educational backgrounds, table 43 clearly shows that the majority of

directors have an economical background (36.0%, this also includes finance). The second and third

most common educational background are engineering and law, with 19.7% and 12.5% respectively.

Broad generalists account for 12.4% of the total sample. When comparing the educational

backgrounds among diversity variable (non-) executive and independent results are similar among

these director classifications. Executive directors are less educated broadly (5.3%) compared to non-

executive (11.3%) and independent (12.7% directors. The results are significant on a significance level

of 5% for a two-tailed independent two sample t-test (p- values are both < 0.001).

When calculating the Blau’s index for the educational background for Europe (‘All’ in table 43), we

treat ‘broad education and ‘other’ also as one category and obtain a value of 0.7926, or standardized

(divided by n) 0.079.

Table 43: Diversity variable educational background

Table with numbers concerning the diversity variable educational background, calculated using SPSS * Excluding missing numbers ** Not excluding missing numbers, these are the missing numbers

All Non-executive Executive Independent

Educational background %* Total %* Total %* Total %* Total

Broad education 12.4% 201 11.3% 181 5.3% 20 12.7% 142

Economics 36.0% 585 29.3% 470 30.4% 115 28.5% 319

Engineering 19.7% 319 15.6% 251 18.0% 68 14.3% 160

History, languages &

philosophy

3.8% 61 3.3% 53 2.1% 8 4.1% 46

Law 12.5% 203 10.7% 172 8.2% 31 10.4% 116

Mathematics & IT 3.1% 50 2.4% 38 3.2% 12 2.8% 31

Medicine 1.4% 22 1.1% 17 1.3% 5 1.3% 15

Political and social

sciences

2.7% 44 2.4% 38 1.6% 6 2.9% 32

Sciences 6.5% 106 5.4% 87 5.0% 19 6.5% 73

Other 2.0% 36 1.5% 24 2.1% 8 1.4% 16

Missing** 18.1% 359 17.0% 273 22.8% 86 15.1% 169

58

4.1.8 Functional Background

When looking at the board directors’ functional backgrounds or track records, the results are,

surprisingly, similar between all directors, non-executive directors and independent directors. The

result are given for all sort of directors in table 44. Approximately 40% of all directors have a financial

background. It must be argued that 12% of all companies in the sample are operating in the financial

services industry, but this 40% average on total number of directors is a lot higher. There is no doubt

that financial expertise is considered to be very important on the board. Listed companies must report

to the public on their financials, and the audit committee must oversee financial reporting and

disclosure. When comparing the proportion of financial profiles between executive (38.5%) and non-

executive directors (43.9%) we find that these numbers are significantly different (two-sided, p = 0.027,

α = 0.05).

The second most common profile (12.7%) is the ‘industrial & technology’ profile. Many directors

possess industrial, technological or scientific knowledge.

Joint third most common categories are marketing, sales & communication and public services. Both

categories play an important role in the service role: attracting resources, reaching people, establishing

links to obtain information, etc. 5.1% of all directors are/were active in the public services sector

(politicians, ambassadors, etc.).

Two categories that are the least common, though not included in the category ‘other’, are human

resources and medicine. The human resources and medicine functions fulfil the service role. Once

again, the categories were chosen after looking at the data, to enhance quality of this research. For

example, medicine was defined separately as most of the directors who were allocated to this

category, were a board member of companies operating in the pharmaceutical industry (defined as

high-tech manufacturing) ,and these directors more specifically had backgrounds in the field of

oncology. The human resource profile is less common, this was also found for Belgium by GUBERNA &

VBO (s.d.) in their two-yearly monitoring research. The Blau’s index was also calculated for functional

background and is 0.79, and 0.056 after normalising for the number of categories.

59

Table 44: Diversity variable functional background

Table with numbers concerning the diversity variable functional background, calculated using SPSS * Excluding missing numbers ** Not excluding missing numbers, these are the missing numbers

Next we will zoom in on several functional backgrounds for several industries and explain the findings

given in table 45. The number of directors mentioned for the industry, is the number of directors

included in the calculations for the Blau’s index (missing values excluded).

The accommodation and food service industry (AF) includes 46 directors who mostly have (43.5%

financial backgrounds and marketing, sales & communication (19.6%) backgrounds. Its normalised

Blau’s index is 0.052. The board’s service role is thus very important to this industry.

The administrative and support service activities industry (AS) includes 34 directors. The most common

functional background is finance (50%), followed by IT (11.8%) and industrial & technology (8.8%). This

percentage for finance is high compared to other industries. It also contains a lot of directors with a

broad track record (19.6%). Its normalised Blau’s index is 0.050.

The construction and real estate industry (CRE) includes 176 directors, who again mostly have financial

profiles (35.2%), but this is not as high as compared to the European average or other industries.

All Non-executive Executive Independent

Functional background % Total % Total % Total % Total

Broad or indecisive 10.8% 207 11.2% 179 7.4% 28 11.2% 125

Energy 4.5% 86 4.1% 66 5.3% 20 4.5% 50

Finance 40.7% 783 38.5% 617 43.9% 166 40.1% 449

Human Resources 0.8% 16 0.8% 13 0.8% 3 1.0% 11

Industrial & Technology 12.7% 245 11.7% 187 15.3% 58 11.7% 131

IT 2.9% 56 3.1% 49 1.9% 7 3.8% 43

Juridical & Legal 3.5% 68 3.7% 60 2.1% 8 3.5% 39

Marketing, sales &

communication

5.1% 98 5.0% 81 4.5% 17 6.2% 69

Medicine 0.6% 11 0.7% 11 4.5% 0 0.9% 10

Public Services 5.1% 98 5.6% 90 2.1% 8 5.7% 64

Real Estate 1.2% 23 1.1% 18 1.3% 5 1.1% 12

Retail 3.6% 70 3.2% 51 5.0% 19 3.5% 39

Telecommunications 1.8% 34 1.6% 26 2.1% 8 2.1% 24

Other 6.8% 130 6.5% 104 6.9% 26 3.3% 37

Missing** 2.9% 57 3.2% 52 1.3% 5 1.4% 16

60

Second most common backgrounds, which can be expected, are industrial & technology (which

contains construction) and real estate. Its normalised index is 0.058.

Next is the electricity, gas, steam, and water supply industry (EGSW), which contains 177 directors.

Only 28.2% are financial profiles, which is rather low when compared to the other industries. Second

most common are energy and public services backgrounds, followed by industrial & technology, and

juridical backgrounds. This clearly reflects the high regulation of the energy and utilities industry. Links

with the government, lawyers and industry experts are important. The government is thus an

important stakeholder/ resource to these companies. Its normalised Blau’s index is 0.059.

The financial and insurance industry (FI) contains 234 directors, of whom 78.2% has a financial

background. This high percentage can be explained because it contains financial profiles for both

accounting/auditing financial expertise, as well as broad finance industry experience (investment,

banking, etc.). This high percentage is significantly different than the average (40.70%) on a 5%

significance level, using a two-tailed two samples t-test (p <0.001). As can be expected, IT and juridical

backgrounds are second and third most common backgrounds. Its normalised Blau’s index is 0.027,

clearly lower than for other industries.

For the information and communication industry (IC) 180 directors were included. Financial profiles

are again most common (38.3%), followed by IT (9.4%), marketing, sales & communications (8.9%),

and telecommunications (7.8%) as can be expected. Rather high values were also found for juridical

and public services profiles (5.6% and 5% respectively) which could point to regulation in this industry

as well, and thus the government may also be an important stakeholder/ resource to this industry.

Also taking into account the important stake for marketing, sales & communication, it is clear that the

service role is of great importance to the information and communication industry. Its normalised

Blau’s index is 0.057.

For the high-tech manufacturing industry (HTM), 335 directors are included. The most common profile

‘finance’ (32.2%) is closely followed by the industrial & technology profile (26.6%). Most other

categories are represented equally (1%-5%). Its normalised Blau’s index is 0.057.

Another industry is the low-tech manufacturing industry (LTM), containing 239 directors. This category

is similar in terms of functional background diversity to the high-tech manufacturing industry, as most

categories are represented equally. The difference between both categories can be found in the

proportions of financial and industrial & technology profiles: the low-tech manufacturing industry

61

contains a higher proportion of financial profiles, and a lower proportion of industrial & technology

profiles. As it is ‘low-tech’, it thus contains fewer industrial and technology profiles. Also, it contains a

large proportion of directors with broad functional backgrounds (13.8%). Its normalised Blau’s index is

0.055.

The mining and quarrying industry (MQ) contains 139 directors. Again, most common are financial

profiles (35.3%). Second and third most common are the industrial & technology profiles (28.1%) and

energy profiles (15.1%). This was expected as the mining and quarrying industry is closely related the

oil industry which contains both industrial & technology and energy profiles. Juridical and public

service profiles are represented equally (4.3%) and again point to the importance of the government

as stakeholder or resource. Its normalised Blau’s index is 0.054.

The professional, scientific and technical activities industry (PST) is an aggregate of the following

businesses: business and other management consultancies, technical testing and analysis, advertising

and marketing, and engineering activities and other related technical consultancies. It contained 56

directors, of whom 39.3% are financial profiles. The second most common profile is the public services

profile (12.5%). This industry is unique in this regard and clearly points towards the importance of the

government as stakeholder or resource. The third most common profile is the industrial & technology

profile. Its normalised Blau’s index is 0.056.

The transporting and storage industry (TS) contains 70 directors, of whom the most common profile

is, again, financial (38.6%). Second most common is the retail profile: evidence suggests that the retail

industry is an important stakeholder: this reflects the retail industry as a supplier. Many of the directors

have a broad track record (15.7%) and juridical and marketing, sales & communication profiles are also

common profiles (7.1%). Its normalised Blau’s index is 0.056.

Last but not least, the wholesale and retail trade industry (WR) contains 227 directors of whom the

largest part are financial profiles (33.5%), followed be retail profiles (20.3%). Marketing, sales &

communication profiles aren’t uncommon (8.8%) and again the government also seems an important

stakeholder or resource (8.8% are public service profiles). Its normalised Blau’s index is 0.058.

We didn’t take a closer look to the public administration and defence industry as it only contained one

company.

62

In terms of functional background diversity, all industries obtain similar values for the normalised

Blau’s index (0.05 -0.06), except for the financial and insurance industry, which scores significantly

lower (0.027).

.

63

Table 45: Functional backgrounds represented across industries

Table with numbers concerning the proportions of functional backgrounds across industries, calculated using SPSS, all missing values are excluded in the calculation of the percentages.

Functional backgrounds

(%)

AF AS CRE EGSW FI IC HTM LTM MQ PST TS WR

Broad / indecisive 19.6 5.9 10.8 11.9 3.4 12.8 10.7 13.8 7.9 10.7 15.7 11.9

Energy - 2.9 1.7 16.4 0.4 0.6 4.5 2.9 15.1 16.1 2.9 1.3

Finance 43.5 50.0 35.2 28.2 78.2 38.3 32.2 40.6 35.3 39.3 38.6 33.5

Human resources 4.3 - - .6 0.9 - 1.2 - - 1.8 - 2.6

Industrial & Technology 4.3 8.8 15.9 10.7 1.7 3.3 26.6 15.1 28.1 8.9 5.7 3.5

IT - 11.8 1.7 1.7 4.3 9.4 3.0 0.4 0.7 3.6 - 2.2

Juridical & Legal 2.2 - 4.5 7.3 3.4 5.6 1.5 3.3 4.3 1.8 7.1 1.3

Marketing, sales &

communication

19.6 - 4.0 0.6 2.6 8.9 3.6 6.3 1.4 5.4 7.1 8.8

Medicine - - - - - - 2.1 0.8 0.7 - 1.4 -

Public services - - 6.3 13.6 1.7 5.0 4.5 2.1 4.3 12.5 2.9 6.2

Real Estate - 2.9 10.2 0.6 - - 0.3 - - - - 0.9

Retail 2.2 - 1.7 0.6 1.3 5.0 - 2.1 - - 10.0 20.3

Telecommunications - 2.9 1.1 0.6 - 7.8 2.1 1.7 1.4 1.8 1.4 0.4

Other 4.3 - 6.8 7.3 1.7 6.7 7.8 10.9 0.7 8.9 7.1 7.0

Total ( absolute number) 46 34 176 177 234 180 335 239 139 56 70 227

64

5 Conclusion

The literature study starts with discussing the importance of corporate governance due to past

malpractices (Enron, Ahold, Parmalat and the financial crisis), followed by the plethora of point views

that exist concerning this broadly discussed topic. The conclusion is that nowadays stakeholders should

at least be included in these definitions and boards should be more diverse. Companies are nowadays

operating in dynamic environments and should therefore represent all of their stakeholders, as they

all matter. Corporate governance should not be driven by compliance, but by engagement. As I believe,

it is nearly impossible for a company to represent all of its stakeholders; a company should carefully

consider who its most important stakeholders are, and represent those stakeholders in the board.

Although the results concerning the impact diversity has on board performance are ambiguous

(positive outcomes are related to higher quality decision-making, negative outcomes are related to

slower decision-making), corporate governance nonetheless acts as a safeguard to prevent

malpractices.

The board, its models and roles are discussed. Concerning board diversity, corporate governance codes

for Belgium, France, Spain and the UK recommend large majorities of non-executive directors or at

least more than half of the board members to be non-executives. These recommendations show that

the unitary board system tries to solve the lack of a dual board system. Also, these recommendations

of large majorities of NEDs support the board’s monitoring role and agency theory: non-executive

directors are expected to be better at monitoring. On the contrary, these recommendations contradict

the stewardship theory, which believes that a majority of executive directors is required: executive

directors do not have conflicts of interest with the owners of the company and have the required

experience. Results show that all countries on average comply with these recommendations and have

majorities of independent non-executive directors. Also, Europe has an average proportion of 80.9%

of non-executive directors. All evidence thus supports the agency theory and the importance of the

board’s monitoring role, while contradicting the stewardship theory.

Harrison & Klein (2007) make an interesting distinction into three sorts of diversity that has proven to

be extremely useful in describing aspects on diversity: diversity as variety, separation and disparity.

The added value of this dissertation can be found mainly in the board members’ functional and

educational backgrounds, both aspects of diversity as variety. The availability of information within the

board is one thing, while sharing information is another thing. As variety becomes more equally and

uniformly spread within the board, unique information becomes more likely to be spread. I believe a

65

more diverse board in terms of diversity as variety, more specifically concerning functional

backgrounds and educational backgrounds, to have two possible impacts on the board’s strategic role:

positive and negative. The upside is higher quality decision-making, while the downside is slower

decision-making. Nonetheless, I strongly believe that these educational and functional backgrounds

reveal what stakeholders are important to the board, and where the company is trying to find its

resources. Functional and educational backgrounds are thus mainly relevant when considering the

stakeholder theory and resource dependency theory, and address the board’s coordinating role and

service role.

This dissertation made a cartography of the mix of profiles within the board of directors within their

environment of top-listed companies within Belgium, France, Spain, and the UK (excluding banks).

Several cross-tabulations show the following results, which could easily be compared across countries

or industries as all of the companies included in the sample have unitary boards.

First of all, all results found in the empirical study for Europe were very similar to the results found in

pre-existing, large-scale studies (conducted by Egon Zehnder, Spencer Stuart) which is evidence for

reproducibility the data. The minority of directors are female (23.5%), as was expected. These female

directors are most likely to be independent non-executive directors. In contrast, women are very

unlikely to hold chairing positions. On average, the European director is 59.9 years old with an age gap

of 5.8 years between executive directors (54.1 years) and non-executive directors (59.9 years).

Similarly, an age gap of 3.1 years was found between independent directors (60.1) and non-

independent directors (57 years). An age gap of 4.8 years was found between chairmen (63.2 years)

and non-chairmen (58.4). Results clearly show that the average age varies with the sort of director. A

proportion of 32.8 % of directors sitting on European boards are non-national directors, and these are

most likely to be independent, followed by non-executive, executive and lastly chairmen. Independent

directors and non-executive directors, important in establishing links (service role) and representing

stakeholders (coordinating role), are thus found more likely to be non-nationals. This evidence

supports that companies nowadays operate in a dynamic and global world and finding resources often

means crossing borders. Findings also stress the importance of independent non-executive directors

on the boards: 59.7% of all directors are deemed independent.

Belgian’s average board size is 12.3. In terms of gender diversity, Belgium performs similarly compared

to Europe. Belgium has the highest proportion of female executives and chairwomen and also has a

high proportion of independent female directors. The average Belgian director is 57.2 years old, no

age differences were found among diversity variables (non-executive versus executive, independent

66

versus non-independent and chairmen versus non-chairmen). In general, Belgium has very high

proportions of non-national directors. Especially high when compared to other countries, are

proportions found for foreign independent directors. We thus conclude that the Belgian board is rather

international and the age of board members does not differ among sorts of directors. The reason that

Belgium boards are international is probably due to the fact that Belgium is a small country and it is

thus relatively easy to cross its borders to find new markets and try to attract resources.

France has an average board size of 14.6, which is largest size found for all four countries. French

boards have the highest proportion of female directors (30.9%), which also reflects that of all four

countries France had the highest set objective. France also has the highest proportions of female

independent non-executive directors and non-executive female directors. Contrary to the previous,

not even one woman was chairing a CAC 40 company (banks were excluded from the sample). The

average board member sitting on a French board is 59.7 years old, although significant age gaps were

found between executive (56.7 years) and non-executive directors (60.1 years) and independent (61.7

years) and non-independent directors (57.5 years). Executive directors and non-independent directors

have lower age averages. Furthermore France has low proportions of non-nationals and high

proportions of non-executives (89.9%) when compared the European average. France thus especially

performs well in terms of gender diversity. The high proportion of NEDs found for France is again

evidence that supports the agency theory. The low proportion of non-national directors compared to

Belgium and the UK can be explained by the fact that French companies, constituents of the CAC 40,

operate in more national environments than Belgian and UK companies, although it must be argued

that all companies included in the sample are global, listed companies operating in international

environments. Possibly, they don’t yet recognize the value of foreign resources and experience as

much as Belgium and the UK do.

The UK has the smallest board size (10.9). The UK performs similarly to the European average

concerning gender diversity and its objective of 25% by 2015 was achieved. Concerning the diversity

variable age, interesting results are found. Though the average age is 59.4 years, very large age gaps

were found. Executives are on average 52.7 years old, and non-executives 59.4 years old. An age gap

was also found between independent (58.8) and non-independent (55.6) directors. The largest age gap

was found between chairmen (64.8) and non-chairmen (56.8): eight years. The UK performs similarly

to Europe concerning proportion of non-national directors, but has the highest proportion of non-

executive non-national directors. Interestingly, although the UK has the lowest proportion of non-

executive directors, it has the highest proportion of independent directors: this clearly reflects the fact

that the UK corporate governance codes only mentions ‘independent non-executive’ directors and

67

doesn’t mention anything specifically on non-executive directors and independent directors

separately. It is clear that the UK values international experience on the boards and that in the UK

independent non-executive directors are valued who are more experienced and possibly have larger

networks, or in other words: who are older. Similarly to Belgium, the UK understands the need to

attract foreign directors to reach new markets.

Spain’s average board size is 13.25. This is different from what the Spencer Stuart Spain Board Index

(2014) found, which included a total of 92 companies, and thus we conclude that the IBEX 35 has a

larger board size than the other (smaller) Spanish companies. Spain performs especially badly

concerning gender diversity: only 17% are women, there are boards without women, no female

executives were found and Spain has the lowest proportion of female independent directors (25.9%).

The average age is 62 years, the largest average found for all four countries. A significant age gap was

only found between executive directors (59.1 years) and non-executive directors (62.5 years). Lastly,

Spain has the lowest proportion of non-national directors. All findings point towards rather old, closed,

national, male board of directors. The Spanish boards especially provide bad numbers for gender

diversity. All findings clearly point towards an old boys’ network preventing women to join the board

and neglecting the importance of foreign directors on the board.

Concerning educational backgrounds, boards are dominated by economic, engineering and law

profiles. These results are consistent with the findings found by GUBERNA & VBO (s.d.) in their two-

yearly monitoring research. Note that the educational background economics, as it is the most

common background, shows the importance of financial reporting and disclosure. Results show a very

large proportion of men for the engineering profile (89.3%). Political and social sciences profiles are

men in 52.3% of cases and therefore contain the highest percentage of women across all educational

backgrounds. Thus, some educational backgrounds are indeed more represented by men than others.

In contrast, all educational backgrounds contain a majority of men and it seems unfeasible that all

professions are more typical of men. Several significant age differences were also found between

educational backgrounds: broad education and economics, broad education and mathematics & IT,

and engineering and economics. Another exceptional finding, compared to other educational

backgrounds, is that medical educational backgrounds are mostly international profiles (71.4%).

When looking at the board members’ functional backgrounds, we see that the most common profile

is without doubt the financial profile (40.7%). This again clearly shows the importance of financial

reporting and disclosure for listed companies and is evidence that supports the board’s monitoring

role. The second most common background is the industrial & technology background. Broad

68

generalists account for 10.8% of all profiles. These broad generalists are more likely to be independent

or non-executive directors than executive directors. As TMT literature believes that team members’

intrapersonal function diversity always has a positive impact on decision-making, and dominant

function diversity only has a positive impact on decision-making when collocated (Cannella, Park &

Lee, 2008), and the board isn’t collocated, these findings clearly suggest that boards should have more

independent or non-executive directors on the board, as they are more likely to have broad

intrapersonal function diversity compared to executive directors and when sitting on the board are

thus more likely to have a positive impact on higher quality decision-making. All functional

backgrounds are dominated by a majority of men, except for the human resources profile. Perhaps the

human resources profile requires a skillset or mind-set, which is very typical of women. The human

resources profile is also the profile with the youngest age. The real estate profile is dominated by 91.3%

men and seems to be more typical of men. Women are second most common to the marketing, sales

& communication functional background, and this category also has lower average age. Several age

gaps were found between functional backgrounds. Directors with a public service background are on

average older than several other functional backgrounds. A possible underlying rationale could be that

older directors have already established larger networks and have more experience, which might be

valued highly in a connection with the government. Consistent with the educational backgrounds, the

medical educational backgrounds contain the highest proportion of non-national directors (72.7%). As

these directors were mostly active in the pharmaceutical industry, the pharmaceutical industry is thus

very international and acknowledges the importance of foreign resources and experience.

Telecommunications and IT are also often international profiles, in 53.3% and 53.2% of cases

respectively.

For the accommodation and food service industry a high proportion of women on the board was found,

compared to other industries. It also has young directors who are mostly national with a financial

background. Furthermore this industry contains the highest proportion of broad generalists (functional

background diversity) across all industries (19.6%). This industry also contains the highest proportion

of marketing, sales & communication profiles (19.6%), which supports the importance of the service

role to this industry.

The administrative and support service activities industry also has young directors, though in this case

mostly international with a financial background (50%) or IT background (11.8%). It seems that

financial knowledge and IT knowledge are valuable information (resources) to this industry, and

international experience and resources are valued highly.

69

The construction and real estate industry mainly values financial profiles, industrial & technology

profiles and real estate profiles. Evidence suggests that the construction and real estate industry is the

most national industry of all industries included in the sample, this is probably due to the nature of the

business: these companies operate in domestic markets.

Also very domestic (88.2% of directors are national) and containing many non-executive directors is

the electricity, gas, steam, and water supply industry. The profile of directors sitting on these boards

clearly reflects the high regulation within this industry is: the highest proportions of legal backgrounds

and public services backgrounds were found, 7.3% and 13.6% respectively. The government is clearly

and important stakeholder to this industry.

The information and communication industry performs similarly to the averages for most variables.

This industry contains many, more or less equally weighted, though important functional backgrounds:

IT, juridical & legal, marketing, sales & communication, retail and telecommunications. This industry

seems confronted with many stakeholders and must obtain information or resources in many different

areas. It is thus not surprising that 12.8% of all backgrounds are broad generalists with broad spectrums

of experience. This industry also seems confronted with regulations and the government as

stakeholder.

Both high-tech- and low-tech manufacturing industries have high proportions of non-executive

directors and the average age is also higher compared to other industries. Results are similar between

both industries: financial profiles are most common, followed by industrial & technology profiles,

although the high-tech manufacturing has a larger proportion of industrial & technology profiles, while

part of these profiles are converted to financial profiles when looking at the low-tech manufacturing

industry.

Interesting results were found for the mining and quarrying industry. It has the highest proportion of

male directors, the highest average age of directors, and a majority of non-national directors. High

proportions of non-executive directors were found for all industrial industries. Furthermore the mining

and quarrying industry also has the highest proportion of independent directors, and evidence

suggests that the government is an important stakeholder. Directors sitting on board of companies in

the mining and quarrying industry mostly have financial and industrial profiles. This industry seems to

operate in an international environment and seems by nature more specific of men.

70

When looking at the professional, scientific, and technical activities industry, an in industry with a large

proportion of independent directors, evidence shows an interesting finding: an above average

proportion of public services profiles. Links with the government, or the government as a stakeholder

seem very important to this industry. Surprisingly this industry has a large proportion of energy profiles

(16.1%). This industry probably offers several consultancy, scientific or technical activities towards the

energy industry. This could also explain why there is a large proportion of public services profiles, as

this was also found for the electricity, gas, steam and water supply industry. The energy industry might

thus be an important stakeholder to the professional, scientific and technical activities industry.

The transporting and storage industry is again dominated by financial profiles. Furthermore this

industry also contains an above average proportion of broad generalist profiles. The transporting and

storage industry has a large proportion of non-executives compared to other industries. Evidence

suggests that the retail industry is an important stakeholder. This can be explained because the

transporting and storage industry closely work together with retail companies to transport, store and

deliver the products.

The wholesale and retail industry mainly contains financial and retail profiles. The government also

seems an important stakeholder or resource to the wholesale and retail industry, and marketing

profiles aren’t uncommon, which also doesn’t come as a surprise. Marketing experience is important

to the retail industry.

As the public and administration and defence industry only contained one company, no conclusions

are drawn specifically for this industry.

For all industries similar values were found concerning the normalised Blau’s index, except for the

financial and insurance industry. This industry, not surprisingly, has a very low value for the Blau’s

index. The financial and insurance industry has a high proportion of men serving on the board, and

contains a large majority of financial backgrounds. When linking this to the financial crisis one might

suggest the failure of this industry is, possibly, reflected in its value for the Blau’s index. Compared to

other industries, which all have values above 0.05 for the normalised Blau’s index, a value of 0.027 for

the financial and insurance industry stands out enormously. As diversity plays an important role as a

safeguard to prevent poor quality decision-making (Sunstein & Hastie, 2015), it becomes clear that this

safeguard simply is not there in terms of functional background diversity in the financial services

industry.

71

This dissertation shows that taking into account functional and educational background clearly adds

value, especially towards understanding the company’s service role and coordinating role. Including

these two variables definitely gives an idea of the several stakeholders who are connected to these

companies or industries.

72

5.1 Limitations

Although this dissertation provides some interesting outcomes and the empirical study certainly has

the advantage of a large sample, it also has several limitations. Firstly, banks were excluded. This is a

strength, but also a weakness. A strength because regulations already strongly determine the mix of

profiles on the boards of banks, a weakness because it wasn’t checked and the sample is actually biased

when drawing conclusions for ‘Europe’. Secondly, the sample only included listed companies. The

sample captures a lot of the market capitalisation, although it does not reflect boards of smaller

companies. Thirdly, when conclusions are drawn for ‘Europe’, we are actually mainly drawing

conclusions for Belgium, France, Spain, and the UK. It must be noted that when outcomes of the

empirical study were compared to results found by other large studies, the outcomes were similar.

Fourthly, no additional data was obtained using interviews because of the size of the sample, which

resulted in several missing values. Fifthly when using the investor triangulation method to increase

objectivity when classifying the data, the opinion of a second researcher was only asked for in case of

doubt. The sample was simply too large for this dissertation to ask another researcher to classify every

single director, and thus no kappa value could be calculated. Sixthly, the outcomes of the research also

depend on the classifications. Companies were classified according to their industries using the NACE

code, a European standard, but for directors’ functional and educational backgrounds no such

classification was used. The advantage of not using a specific classification system is, especially for

functional background, that classification categories could be chosen towards maximal workability

with the data.

5.2 Further research

Several opportunities for further research exist. Firstly, performing similar research on different

samples: two-tier boards, different countries, SMEs, etc. As the research in this study focussed on a

quantitative analysis, though rather exploratory, further research could scrutinize more thoroughly

several outcomes of this research for the different countries/industries. When doing research on

specific industries, smaller industry-specific samples could be used using interview methods, to find

and check the underlying drivers for these diversity characteristics.

Secondly, when comparing the Blau’s indexes for the several industries, further research might

investigate the complete financial and insurance industry (including banks) separately. I suggest

further research also continues to work with the three diversity dimensions given by Harrison & Klein

(2007), as I believe it contributes to explaining the outcomes of results.

73

Thirdly, further research could also establish a link between the diversity characteristics found for

industries/countries and performance. More specifically I believe further research could investigate

whether the value obtained for the Blau’s index (for diversity as variety: educational background,

functional background) has an impact on performance, and can maybe function as a safeguard.

Lastly, when similar studies are regularly performed, further research could also study trends for

diversity variables as educational backgrounds and functional backgrounds or track records.

XI

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XVI

Appendix 2.1

BEL 20

Included companies HQ Industry

Anheuser-Busch Inbev Belgium Manufacturing low tech

Ackermans & van Haaren Belgium Financial and insurance activities

Ageas Belgium Construction and real estate

Befimmo Belgium Construction and real estate

Bekaert Belgium Manufacturing low tech

Bpost Belgium Transporting and storage

Cofinimmo Belgium Construction and real estate

Colruyt Belgium Wholesale and retail trade

Delhaize Group Belgium Wholesale and retail trade

D’Ieteren Belgium Wholesale and retail trade

Elia Belgium Electricity, gas, steam, and water supply

Groupe Bruxelles Lambert Belgium Financial and insurance activities

GDF Suez France Electricity, gas, steam, and water supply

Proximus Belgium Information and communication

Solvay Belgium Manufacturing high tech

Telenet Group Belgium Information and communication

Union chimique belge Belgium Manufacturing high tech

Umicore Belgium Manufacturing low tech

Excluded companies

Delta Lloyd Excluded because it has a two-tier board structure.

KBC Group Excluded because of banking activities.

XVII

CAC 40


Accor France Accommodation and food service activities

Air Liquide France Manufacturing high tech

Airbus Group The Netherlands Manufacturing high tech

Alcatel-Lucent France Manufacturing high tech

Alstom France Manufacturing low tech

ArcelorMittal Luxembourg Manufacturing high tech

Bouygues France Construction and real estate

Cap Gemini France Information and communication

Carrefour France Wholesale and retail trade

Danone France Manufacturing low tech

Électricité de France France Electricity, gas, steam, and water supply

GDF Suez France Electricity, gas, steam, and water supply

Essilor International France Manufacturing low tech

Gemalto The Netherlands Information and communication

Kering France Manufacturing high tech

LafargeHolcim Switzerland Information and communication

Legrand France Manufacturing high tech

L’Oréal France Manufacturing high tech

Moët Henessy Louis Vuitton France Wholesale and retail trade

Orange France Information and communication

Pernod Ricard France Manufacturing low tech

Renault France Manufacturing high tech

Safran France Manufacturing high tech

Saint Gobain France Wholesale and retail trade

Sanofi France Manufacturing high tech

Schneider Electric France Manufacturing high tech

Solvay Belgium Manufacturing high tech

Technip France Mining and quarrying

Total France Manufacturing low tech

Valeo France Manufacturing high tech

Veolia Environment France Electricity, gas, steam, and water supply

Vinci France Construction and real estate

Excluded companies

XVIII

AXA Excluded because of banking activities.

BNP Paribas Excluded because of banking activities.

Crédit Agricole Excluded because of banking activities.

Michelin Excluded because it has a two-tier board structure.

Publicis Groupe Excluded because it has a two-tier board structure.

Société Générale Excluded because of banking activities.

Unibail-Rodamco Excluded because it has a two-tier board structure.

Vivendi Excluded because it has a two-tier board structure.

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IBEX 35


Abengoa Spain Construction and real estate

Abertis Spain Transporting and storage

Acciona Spain Construction and real estate

Actividades de Construcción y Servicios

Spain Construction and real estate

Amadeus Spain Information and communication

ArcelorMittal Luxembourg Manufacturing high tech

Bolsas y Mercados Españoles Spain Financial and insurance activities

Dia Spain Wholesale and retail trade

Enagas Spain Transporting and storage

Ferrovial Spain Construction and real estate

Gamesa Spain Electricity, gas, steam, and water supply

Gas Natural Spain Electricity, gas, steam, and water supply

Grifols Spain Manufacturing high tech

International Airlines Group United Kingdom Transporting and storage

Iberdrola Spain Electricity, gas, steam, and water supply

Inditex Spain Wholesale and retail trade

Indra Sistemas Spain Information and communication

Jazztel United Kingdom Information and communication

Mapfre Spain Financial and insurance activities

Mediaset Spain Information and communication

Obrascón Huarte Lain Spain Construction and real estate

Red Électrica de España Spain Electricity, gas, steam, and water supply

Repsol Spain Manufacturing low tech

Sacyr Vallehermoso Spain Construction and real estate

Técnicas Reunidas Spain Professional, scientific and technical activities

Telefónica Spain Information and communication

Viscofan Spain Manufacturing low tech

Excluded companies

Banca Popular Excluded because of banking activities.

Banca Sabadell Excluded because of banking activities.

Banca Santander Excluded because of banking activities.

Bankia Excluded because of banking activities.

Bankinter Excluded because of banking activities.

BBVA Excluded because of banking activities.

XX

Caixabank Excluded because of banking activities.

Fomento de Construcciones y Contratas

No corporate governance report published for 2014.

XXI

FTSE 100


3i Group United Kingdom Financial and insurance activities

Aberdeen Asset Management United Kingdom Financial and insurance activities

Admiral Group United Kingdom Financial and insurance activities

Aggreko United Kingdom Manufacturing high tech

Anglo American United Kingdom Mining and quarrying

Antofagasta United Kingdom Mining and quarrying

ARM Holdings United Kingdom Financial and insurance activities

Ashtead Group United Kingdom Administrative and support activities

Associated British Foods United Kingdom Wholesale and retail trade

AstraZeneca United Kingdom Manufacturing high tech

Aviva United Kingdom Financial and insurance activities

Babcock International Group United Kingdom Public administration and defence

Bae Systems United Kingdom Manufacturing high tech

BG Group United Kingdom Mining and quarrying

BHP Billiton United Kingdom Mining and quarrying

British Petroleum United Kingdom Mining and quarrying

British American Tobacco United Kingdom Manufacturing low tech

British Land United Kingdom Construction and real estate

British Telecommunications Group

United Kingdom Information and communication

Bunzl United Kingdom Transporting and storage

Burberry Group United Kingdom Manufacturing low tech

Capita United Kingdom Professional, scientific and technical activities

Centrica United Kingdom Electricity, gas, steam, and water supply

Coca-Cola Hellenic Bottling Company

Switzerland Manufacturing low tech

Compass Group United Kingdom Accommodation and food service activities

Cement Roadstone Holdings Ireland Manufacturing low tech

Diageo United Kingdom Manufacturing low tech

Direct Line Inurance Group United Kingdom Financial and insurance activities

Dixons Carphone United Kingdom Wholesale and retail trade

EasyJet United Kingdom Transporting and storage

Experian Ireland Administrative and support activities

Friends Life Group United Kingdom Financial and insurance activities

G4S United Kingdom Administrative and support service activities

XXII

Guest, Keen and Nettlefolds United Kingdom Manufacturing high tech

GlaxoSmithKline United Kingdom Manufacturing high tech

Glencore Switzerland Mining and quarrying

Hammerson United Kingdom Construction and real estate

Hargreaves Lansdown United Kingdom Financial and insurance activities

International Airlines Group United Kingdom Transporting and storage

IMI United Kingdom Manufacturing high tech

Imperial Tobacco Group United Kingdom Manufacturing low tech

Intercontinental Hotels United Kingdom Accommodation and food service activities

Intertek Group United Kingdom Professional, scientific and technical activities

Intu Properties United Kingdom Construction and real estate

Independent Television United Kingdom Information and communication

Johnson Matthey United Kingdom Manufacturing low tech

Kingfisher United Kingdom Wholesale and retail trade

Land Securities Group United Kingdom Construction and real estate

Legal & General Group United Kingdom Financial and insurance activities

London Stock Exchange United Kingdom Financial and insurance activities

Marks & Spencer Group United Kingdom Wholesale and retail trade

Meggitt United Kingdom Manufacturing high tech

Mondi Austria Manufacturing low tech

Morrisons United Kingdom Wholesale and retail trade

National Grid United Kingdom Electricity, gas, steam, and water supply

Next United Kingdom Wholesale and retail trade

Pearson United Kingdom Information and communication

Persimmon United Kingdom Construction and real estate

Petrofac United Kingdom Mining and quarrying

Prudential United Kingdom Financial and insurance activities

Randgold Resources Jersey Mining and quarrying

Reckitt Benckiser Group United Kingdom Manufacturing low tech

Reed Elsevier United Kingdom Information and communication

Rio Tinto United Kingdom Mining and quarrying

Rolls-Royce Holdings United Kingdom Manufacturing high tech

Royal Dutch Shell The Netherlands Mining and quarrying

Royal Mail United Kingdom Transporting and storage

Royal and Sun Alliance Insurance Group

United Kingdom Financial and insurance activities

SABmiller United Kingdom Manufacturing low tech

XXIII

Sage Group United Kingdom Information and communication

Sainsbury United Kingdom Wholesale and retail trade

Schroders United Kingdom Financial and insurance activities

Severn Trent United Kingdom Electricity, gas, steam, and water supply

Shire Ireland Manufacturing high tech

Sky United Kingdom Information and communication

Smith & Nephew United Kingdom Manufacturing high tech

Smiths Group United Kingdom Manufacturing high tech

Sports Direct International United Kingdom Wholesale and retail trade

Scottish and Southern Energy United Kingdom Electricity, gas, steam, and water supply

St. James’s Place United Kingdom Financial and insurance activities

Standard Life United Kingdom Financial and insurance activities

Tesco United Kingdom Wholesale and retail trade

Travis Perkins United Kingdom Wholesale and retail trade

Tullow Oil United Kingdom Mining and quarrying

Unilever The Netherlands Wholesale and retail trade

United Utilities Group United Kingdom Electricity, gas, steam, and water supply

Vodafone Group United Kingdom Information and communication

Weir Group United Kingdom Professional, scientific and technical activities

Whitbread United Kingdom Accommodation and food service activities

Wolseley United Kingdom Wholesale and retail trade

Wire and Plastic Products United Kingdom Professional, scientific and technical activities

Excluded companies

Barclays Excluded because of Banking activities.

Carnival Corporation Excluded because of HQ in USA, outside Europe.

Fresnillo Excluded because of HQ in Mexico, outside Europe.

HSBC Holdings Excluded because of banking activities.

Lloyds Banking Group Excluded because of banking activities.

Old Mutual Excluded because of banking activities.

Royal Bank of Scotland Excluded because of banking activities.

Standard Chartered Excluded because of banking activities.

Touristik Union International Excluded because of two-tier structure.

XXIV

Appendix 3.2

Functional background or track record classification: elements

Broad or indecisive professional background Several elements combined.

Energy Electricity, oil, gas, and utilities

Finance Economics, insurance, accounting, investment, tax,

chartered accountant, and chief financial officer

Human resources Human Resources

Industrial & technology Manufacturing, engineering, construction, mining,

sciences, pharmaceuticals, and aerospace

IT IT, mobile

Juridical & legal Lawyer, solicitor

Marketing, sales & communication Media, marketing, sales, and public relations

Medicine Medicine, healthcare

Public services Politician, diplomat, ambassador, secretary of state,

and army

Real estate Real estate , chartered surveyor

Retail Retail

Telecommunications Telecommunications

Other Post, corporate governance, rental, food &

beverage, facility management, defence, security,

tobacco, designer, hospitality, transport & glass

expertise

XXV

Appendix 3.3

Educational background classification: elements

Broad education PPE (Philosophy, Politics and Economics), several

elements combined

Economics Economics, accounting, finance, and business

administration

Engineering Civil engineering, industrial engineering, mechanical

engineering, electrical engineering, chemical

engineering, electro mechanics, and technology

History, languages & philosophy Modern history, languages, philosophy, and classics

(philosophy, Greek, and Latin)

Law Law

Mathematics & IT Mathematics, IT, statistics, and operational

research

Medicine Medicine

Political and social sciences Sociology, politics, and psychology (excluding

economics)

Sciences Physics, chemistry, geology, and biology

Other Social work, architecture, education, foreign

services, real estate, nutrition, theology, and

telecommunication studies

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