Gender diversity in Top Management Teams in The Netherlands; Does Industry Matter?

Gender diversity in Top Management Teams in

The Netherlands; Does Industry Matter?

MSc. International Business & Management

Name:

Nynke Jongstra

Studentnr:

s1770551

Contact:

n.jongstra@student.rug.nl

Supervisor:

Dr. K. van Veen

Referent:

Ms. M. Onrust

Faculty of Economics and Business

University of Groningen

Gender diversity in Top Management Teams in The

Netherlands; Does Industry Matter?

Keywords: Top Management Teams, industry characteristics, gender diversity

This study expands research done by Brammer, Millington, and Pavelin (2007) by analysing the differences in the level of gender diversity in Top Management Teams across industries. This will be tested on 67 of the largest companies in The Netherlands by looking at the Top Management Teams consisting of the executive boards and non-executive boards according to the two-tiered system as prescribed by the Dutch law. The test was conducted with data from the years 2007-2011, ranging from 2 years before till 2 years after the implementation of the gender quota in The Netherlands.

CONTENT

2.1 The Current Situation 7

... 2.2 Gender Diversity 8 ... 2.3 The Netherlands 10 ... 2.3 Industries 11 ... 2.3.1 Recruitment Base 12 ...

2.3.2 Composition of the Workforce 12

... 2.3.3 Stakeholders 13 ... 2.3.4 Non-Executives 14 ... 2.3.5 Level of Technology 15 ... 3.METHODOLOGY 16 ...

3.1 Dependent Variable - Level of Gender Diversity 16

...

3.1.1 Level of Gender Diversity 16

...

3.1.2 Top Management Team 16

...

3.2 Independent Variable - Type of Industry 17

... 3.2.1 Industry Variables 17 ... 3.3 Control Variable 18 ... 3.4.1 Industry Size 18 ...

3.4.2 Top Management Team Size 18

... 3.5 Data 19 ... 4.RESULTS 20 ... 4.1 Assessment of correlations 20 ...

4.2 Multiple Linear Regression Analysis 22

... 4.2.1 The level of gender diversity in Top Management Teams in 2007 and the IVs. 22

... 4.2.2 The level of gender diversity in Top Management Teams in 2011 and the IVs 23

...

4.2.3 The increase in the level of gender diversity ‘07 -’11 and the IVs 24

...

5.CONCLUSION&DISCUSSION 26

...

5.1 Discussion of results 26

...

5.2 Limitations and further research 27

1.INTRODUCTION

Previous studies have shown that women are remarkably underrepresented in the most influential Top Management Teams, which can exclude them from opportunities to develop leadership competences, including decision-making and agenda-setting (Beggs, Doolittle, 1993; Burgess, Tharenou 2002; Farrell, Hersch 2005; Catalyst, 2006; Hillmann, Cannella 2007; Schein, 2007; a.o.). To illustrate this, Cappelli and Hamori (2005) conducted a research among Fortune 500 companies of the year 2001. They concluded that, in 2001, only 11 per cent of executive positions were held by women. This number increased only slightly over the years, as in 2006 this number only amounted up to 14.7% (Catalyst, 2006).

The 2006 Catalyst research also shows that from 1995 to 2005, the average increase in female Top Management Team representation of Fortune 500 companies only amounted up to 0,5 per cent per year. From these numbers, it can be concluded that it will take approximately 70 years to reach the 50 per cent balance, to reach gender equality.

Debates on this topic of inequality are fierce (Masters, 2008). The European Union, for example, has threatened to implement a 40 per cent target rate for female employment by 2010. Due to disappointing progress in this area, however, in July 2011 they changed this into voluntary targets of 30% of Top Management Teams consisting of women by 2015, and 40% by 2020 (ec.europa.eu; 10-10-2012). The high levels of agitation caused by this discussion, leads companies to fear the negative publicity that might arise when they have very low levels –or even absence- of women on Top Management Teams (Daily and Dalton, 2003). Therefore, companies might be forced to introduce a female quota even before the actual implementation of the gender quota at a national level. This way, companies can evade the risk of becoming less interesting to investors as a result of low levels of female representation on Top Management Teams (Daily and Dalton, 2003; Masters, 2008).

A lot of research has already been done in this area. Yet, most research done on this topic is related to nationality diversity in Top Management Teams (Staples, 2007; Van Veen and Marsman, 2008). Or in the area of the organisational predictors for women on corporate Top Management Teams (Beggs, Doolittle, 1993; Burgess, Tharenou 2002 ; Hillmann, Cannella, 2007; a.o.). Gender diversity in Top Management Teams, however, still received insufficient attention to draw solid conclusion. Especially when related to industry-specific characteristics, a good research base is lacking (Bertrand and Hallock, 2001; Singh et al, 2001; a.o.).

In this research I will make two assumptions based on the existing literature. First of all, it is expected that certain industries will have more gender diversity in their Top Management Teams than others because of industry characteristics. Moreover, certain industries will experience more pressure from outsiders to meet the legislation on gender quota than others. Reason for the latter, is that stakeholders might increase the pressure for companies to comply with legislation in certain industries more than others.

previous studies by using the Database of Mr van Veen, thereby providing more accurate and ‘generalisable’ results. Secondly, this research contributes to the existing literature by making a comparison between the different industries, which enables me to highlight the importance of several industry-specific influences on board diversity compared to cultural-, or firm-specific characteristics as described in previous research.

1.1 Problem statement

According to numbers of the International Labour Organisation (ILO), there has been an increase in female involvement in the labour force. Women are, however, still underrepresented in management roles, and are still mainly assigned to low-wage and lower ranked jobs (ILO, 2004). In a more recent study, the ‘Sociaal Cultureel Planbureau; (Social cultural planning agency; SCP) reports on a ‘gradual but slow increase’ in the number of women in Top Management Teams (Berkel, M. van, Merens, A. 2011). Many studies have, therefore, investigated the factors influencing female representation in Top Management Teams and the trend of diversity in Top Management Teams (Singh, Vinnicombe, 2004; Brammer et al. 2007; Grosvold et al. 2007; Ling, Kellermans 2010; Rivas, 2012).

Flynn and Adams (2004) show the importance of observing the type of industry a company is operating in when analysing female Top Management Team participation: public utilities, financial services and insurance industries are found to be the industries with the highest number of female directors compared to the technology and the manufacturing/industrial sectors. In those sectors, in many instances, no female directors were to be found at all. As Harzing, Myloni and Mirza found out in their 2004 research, multinational companies tend to align their human resource strategies to the industry in which they operate. In addition, Chung, Kerkhofs and Ester (2007) show that firms with relatively many female workers are active in the public and services sector. It therefore remains interesting to study whether the number of women in European, and especially among Dutch Top Management Teams is influenced by the type of industry the company is operating in.

In this study, I will address the large differences in employment between men and women, among different industries in The Netherlands. I will try to determine the relationship between industry type and the increase in number of women on Top Management Teams.

For the purpose of this research, Top Management Team is defined as the executive board and members of the supervisory board.

Put shortly, I want to find out whether industry type is related to the amount of women in Top Management Teams. Moreover, I want to analyse whether the amount of time it takes for a company to increase the number of women on their Top Management Teams differs across industries as I expect there to be large differences across industries due to external pressures, and other industry specific characteristics. Companies will be categorised according to their SIC industry classification.

and if there are significant differences between the amount of women in corporate Top Management Teams. The company selection is derived from AEX, AMX, and ASMX listed companies which are registered in the European Top Management Team Project database. The number of companies analysed was reduced to 67 due to non-available data.

This implementation time is expected to be different across multiple industries as certain industries will not experience any external pressure to increase the level of gender diversity on their Top Management Teams. For companies who do experience this type of pressure, it is expected that the debate around the issue will be enough for the company to take precautionary measures. This study is an extension of the Brammer et al. (2007) research on UK firms, put in the context of voluntary gender quota and different cultural values. Not only will I try to identify which industries have higher levels of gender diversity in their Top Management Teams, I will also look at the increase in these levels between industries.

For the purpose of this research, I will first provide some theoretical background on the subject at hand. This analysis of the theory is set out on several levels. To begin with, a general overview will be provided of the situation involving gender inequality. Proceeding throughout this thesis, it will gradually evolve in a more specific analysis of the variables involved and eventually, hypotheses will be drawn and tested. In the last section of this thesis I will provide a discussion around the conclusions which can be drawn from this research.

1.2 Research question

2.THEORETICAL BACKGROUND

In the following section, a theoretical background is given of the existing literature concerning the main research topic. First, an overview of the current situation will be given, in order to provide a better understanding of the subject. After that, an analysis of the literature concerning the pros and cons of gender diversity in Top Management Teams will be provided. In the remainder of this paragraph, information on the country under consideration -The Netherlands- will be explained in relation to the literature. Also, the focus of analysis will further be narrowed to the industry level. From this analysis hypotheses will be drawn which will be tested in later sections of this paper.

2.1 The Current Situation

Meyerson and Fletcher, tried to predict the future in 2002 through their research on female representation in Top Management Teams. In their analysis, they indicate that in the late 1990s, women came across many hurdles when trying to pursue a career. They were typically employed in low-pay, low-status, and gender-segregated jobs (Davidson and Cooper, 1992). Meyerson and Fletcher, however, saw possibilities for women to progress to a fair distribution of top positions. They claimed that in the coming millennium, women would be holding seats on corporate boards, and running major firms. Evidence of the first decade of ‘this new millennium’, however, paints a considerably different, and more negative, picture. The German Magazine ‘Der Spiegel’ comments in August 2012 on strikingly low rates of women in German Top Management Teams, only amounting up to 3 per cent. While in the ‘Global Gender Gap report 2011’ -an ongoing study among 135 countries worldwide on the magnitude and scope of gender-based disparities- issued by the Economic Forum can be read that in The Netherlands this number barely reaches the 4 per cent barrier.

For many years women have shown to be striving to decrease the professional gap that exists between them and their male counterparts by fighting for equal rights for both men and women (Gerstein, Lichtman and Barokas, 1988). However, women are still so heavily underrepresented in Top Management Teams, that the business world is being criticised for upholding the glass ceiling, which hinders women to reach the upper echelons of organisations (Ruigrok, Peck, Tacheva, Greve and Hu, 2006). Over the years, this fact has caused many people to raise their voice for some sort of positive discrimination in order to improve this balance.

The call for positive discrimination began in the political arena. As a sort of exemplary function for other industries, governments of several countries imposed quota for political parties. Nowadays, about 45 countries world wide have introduced electoral gender quotas by law, while in another 50 or so countries some political parties have implemented voluntary party quotas into their statutes. With all these regulations and quota in politics, pressure on the corporate world has risen.

binding gender quota which apply to all boards of public limited companies, and other companies, including state and municipality owned companies and cooperative companies (comparable to the Norwegian legislation). Other countries, like The Netherlands and Spain have introduced a ‘softer’ type of legislation which lacks sanctions. As mentioned before, in Germany, there is a fierce debate around this topic and legislation is in preparatory stages. Other countries have imposed regulations that only concern state owned companies (Denmark, Finland, Greece, Austria, and Slovenia).

2.2 Gender Diversity

In this paragraph, an overview is given of the possible up- and downsides of gender diversity in Top Management Teams.

Lew Platt, former CEO of Hewlett Packard, (quoted from Kochan, Bezrukova, Ely, Jackson, Joshi, Jehn, Leonard, Levine, and Thomas, 2003), says:

“I see three main points [...] for diversity. First, a shortage of talent that requires us to seek out and use the full capabilities of all our employees. Second, the need to be like our customers, including the need to understand and communicate with them in terms that reflect their concerns. Third, that diverse teams produce better results.”

In the aforementioned citation of Mr Platt, he touches upon the main reasons for a company to increase the level of women on the Top Management Team. The quotation is also just one example that illustrates what many previously conducted studies have indicated already; the dominance of men in Top Management Teams, and the under-representation or even absence of women in Top Management Team functions (Metcalfe, 2001).

A good summary on the topic under investigation is given by Terjesen, Sealy and Singh (2009). They review existing literature on how gender diversity in Top Management Teams impacts corporate governance outcomes and indicate that future researchers should take into account multiple levels of analysis, such as industry/environment, company and board. Research of the European Council subscribes adds to this, by saying: “Gender imbalance on corporate Top Management Teams remains an important challenge for all EU Member States. It constitutes an untapped potential of skilled human resources, as evidenced by the discrepancy between the high number of female graduates and their underrepresentation in top-level positions. As women still face numerous barriers on the way to the top, this discrepancy can be seen as a waste of much highly-qualified and needed human resources.” (European Commission- Directorate-General for Justice, 2012) Hereby indicating that companies will not maximise their potential until higher levels of gender diversity are implemented in Top Management Teams.

values in Top Management Teams will have an influence on Top Management Team performance. Following this reasoning, in order to enhance its performance, companies should induce diversity in their Top Management Teams. For the purpose of this research, diversity is defined according to Arfken et al (2004): “[diversity is defined as the] differences that are associated with age, physical appearances, culture, job function or experience, ability, ethnicity, personal style, gender and religion.”

The aforementioned contributions of higher levels of gender diversity in Top Management Teams can be summarised in two types of considerations, namely the ethical considerations (Burke, 2000; Singh et al, 2001) and the economic considerations which both relate to equal opportunity and equal representation (Brammer et al. 2007). The ethical considerations are, as described before, mainly concerned with the seemingly homogeneous nature of Top Management Teams which do not at all reflect the societies companies operate in and the unfairness of exclusion of women for certain positions (Burke, 2000; Westphal, Milton 2000). The outcomes of acting according to the ethical considerations will mainly be beneficial to society, in the sense that it will result in a fairer society.

The economic arguments supporting gender diversity in Top Management Teams argue that by making use of the full potential – i.e. exploit the opportunities diversity offers you- the company can select the best person for the job, which will be beneficial to the financial results of the firm. For the consumer oriented industries, a diversified board will be a better representation of the customer-base and the other key-stakeholder groups, which will be a positive signal to investors and employees resulting in loyalty and motivation (Burgess, Tharenou, 2002; Bilimoria, Wheeler, 2000; Catalyst, 1995).

Many scholars subscribe to this, by suggesting the unique ability which women could offer to Top Management Teams (Ryan and Haslam, 2006). In many instances, women are the main consumer of products and services. And in this capacity, they can bring a different perspective, experience and a new way of offering products to markets which are customised to the specific needs of its customers. In a way, they are the representatives of the customer base of a company (Kramer et al, 2006; Adams and Ferreira, 2004; Singh and Vinnicombe, 2004; Dwyer et al, 2003; Daily et al, 1999).

Of course, there are also studies who claim the opposite, i.e. that Top Management Team diversity is not beneficial to firm performance (Carter, D.A. D’Souza, F. Simkins, B.J. Simpson, W.G. 2010), or even claim that the appointment of women has a negative effect (Lee, P.M. James, E.H. 2007). One of the arguments is that diversity in Top Management Teams might be a disadvantage in terms of group performance (Erhardt, N.L. Werbel, J.D. Shrader, C.B. 2003). The higher levels of diversity lead to greater time and effort required to reach consensus within a team (Hambrick et al, 1996; Knight et al, 1999). Therefore, homogeneous Top Management Teams will outperform heterogeneous teams.

2.3 The Netherlands

When considering the subject matter at the national level, one cannot help but notice that the members of the European Union are heavily influenced by measures imposed by the European Council. For the purpose of this study, however, we will look at The Netherlands at the national level, as there are only few regulations imposed from EU level. The main reason for looking at this from the national level is that the regulations on gender equality are imposed voluntarily by the Dutch government, and at their own initiation. Until March 2012 – when the ‘women on Top Management Teams pledge’ was introduced- there were no targeted initiatives. From March 2012 onwards, the Commission started to explore policy options for targeted measures to enhance female participation in decision-making, in the case of insufficient progress through self-regulation by member states (European Commission- Directorate-General for Justice, 2012). As this process has only been started in March 2012, no results had been made public yet at the time of writing this thesis, and no far stretching measures have been imposed. So the focus of this study will remain on the national level.

At the national level, several studies have been done in order to identify the factors influencing gender diversity in Top Management Teams (Hillmann, Cannella 2007; Burgess, Tharenou 2002; Beggs, Doolittle, 1993; Fortmann, 2010; a.o.).

The ‘Sociaal Cultureel Planbureau’ (SCP), together with the ‘Centraal Bureau voor de Statistiek’ (CBS) reports on the current situation of gender diversity in Top Management Teams in The Netherlands. In their report ‘Emancipatiemonitor 2010’, they show that the 25 largest companies in The Netherlands had no females in their Top Management Teams at all in 2007, whereas this percentage has increased to 5.6 per cent in 2010.

Koopman, Den Hartog and Konrad show in their 1999 study that a woman’s position and career perspective is largely influenced by the extent to which people perceive women in management teams to be normal. In their article, the authors state that the difference in perceptions is largely influenced by the degree to which the perception of traditional gender roles prevails in a society, which in turn are defined by cultural values.

employees are being perceived and perceive themselves. In a qualitative research by Yerkes, Standing, Wattis and, Wain (2010) based on 67 interviews done in 2005 and 2006, the authors investigate the work-life balance for women in the UK and in The Netherlands. The study reports on ‘the decline of the nuclear family’, where the embeddedness of traditional gender roles as mentioned by Koopman, Den Hartog and Konrad in their 1999 study disappear and care roles will not automatically be taken care of by women. Alongside to this movement, the female labour force has increased since the 1970s. Nowadays, the OECD (2007) reports on women’s labour market participation in The Netherlands of 69%. In the same report, however, can be read that these numbers may seem high, but mask the fact that most of these women (61 per cent) work on a part-time basis.

A lot of measures have been put in place in The Netherlands in order to stimulate female labour participation. And even though the Dutch labour market remains gendered, the reduced working hours model implies that both partners take responsibility for work and care. According to van Klaveren, Salverda, and Tijdens in their article on ‘Retail jobs in The Netherlands: Low pay in a context of Long-term wage moderation’ (2009), The Netherlands is the “world champion of part-time employment, and the OECD champion of youth employment growth” (Klaveren, M. Van Salverda, W. Tijdens, K. 2009; 413). Due to the high level of part-time jobs, employment has become more accessible over the years. In order to further encourage the female labour participation, legislation like the ‘Working Hours Adjustment act’ (Wet Aanpassing Arbeidsduur - WAA), and the ‘Dutch Work and Care act’ (Wet Arbeid en Zorg) have been put in place in order to facilitate more flexible working hours for both men and women so they can distribute work and care taking equally amongst each other (Yerkes, M. Standing, K. Wattis, L. Wain, S. 2010) which will enhance equality among men and women which will in turn enable women to be part of the workforce.

Further, and more specific, governmental measures were put in place to foster the work environment for women in The Netherlands which might result in higher levels of female labour participation and maybe even higher levels of gender diversity in Top Management Teams in The Netherlands. The most pronounced form of these governmental measures, legislation on gender quota, was accepted by the Dutch government by the end of 2009. The timeline of this study will be centred around this implementation. As the mere fact that the government discussed the possible implementation of legislation on gender quota might have been enough for certain companies and their stakeholders to increase the pressure, the timeframe in which this study is being conducted starts in 2007, two years before the actual implementation of the gender quota. Across this timeline, the levels of gender diversity in Top Management Teams are expected to increase for two reasons. First of all, because of the (partially) compulsory character of the gender quota. So companies are obliged to do so by law. And secondly, because of expectations and pressures from stakeholders. These pressures are expected to be higher for certain industries than for others.

2.3 Industries

2.3.1 Recruitment Base

On the industry level, many scholars confirm that the lack of gender diversity on the industry level originates from the mere fact that women cannot apply for appropriate jobs in that particular industry. Burke (1997) follows the line of reasoning that women have difficulty getting in to certain positions as the pool of qualified candidates for the management position is often drawn from people who already hold similar positions in other large companies (Norburn, 1989), with an advantage for people who hold experience in the industry of the vacant position. Complementary to that is the argument provided by the Social Identity Theory which explains why, when appointing a candidate for a new director’s position, men still draw men with whom they have more similarity (Singh and Vinnicome, 2004). Apparently, candidates are drawn from the same base, over and over again. This implicates that success of the company is still seen as the accomplishment of male behaviour, which implies that sex stereotyping is still widely used as a prevailing mindset in the recruitment process (Emerald Insight, 2006). According to the human capital theory “managers gain education and experience, [which allows them to] gain [a] greater mastery of management skills and perspectives” (Schultz, 1961; Becker, 1964). The existence of all the aforementioned factors will deny women the experience of high-level visibility assignments which would allow them to be “branded as successful” and get appointed to the higher level jobs (Catalyst, 2004). This human capital requirement is more difficult to live up to in industries few females operate in. It is therefore to be expected that, as candidates for the Top Management selection process will be withdrawn from the same pool of people who have been working in that particular industry for years, newly appointed managers will be representative for the workforce and similar to the people who are already in the Top Management Team. One can therefore assume that industries which already had higher levels of gender diversity on their Top Management Teams in the past, will also appoint more women in the Top Management Teams.

H1a : The number of women already appointed in Top Management Teams in 2006 is positively related to the

level of gender diversity in TMTs in 2007.

H1b : The number of women already appointed in Top Management Teams in 2010 is positively related to the

level of gender diversity in TMTs in 2011.

2.3.2 Composition of the Workforce

Following this line of reasoning, Burke (1997) concludes that the composition of the pool of candidates will be systematically related to the composition of the wider workforce associated with the industry in question. This assumes the influence of prejudice and stereotyping in these companies which will eventually result in perceiving females as less appropriate for the job (Beggs and Doolittle, 1993). Also, their criterion of industry experience will tend to favour male candidates in the selection process (Burke, 1997).

(Koopman, et al. 1999). Following the reasoning of Brammer et. al (2007), less gender diversity is expected to be found in Top Management Teams of companies which operate in industries with low gender diversity among their workforce.

H2a : The level of gender diversity in the workforce of an industry is positively related to the number of

women in Top Management Teams in the industry in 2007.

H2b : The level of gender diversity in the workforce of an industry is positively related to the number of

women in Top Management Teams in the industry in 2011.

H2c : The level of gender diversity in the workforce of an industry is positively related to the increase in

gender diversity in Top Management Teams in that industry between 2007 and 2011.

2.3.3 Stakeholders

When a company strives for a better representation of its industry’s stakeholders, it is expected that certain industries have higher levels of gender diversity in their Top Management Teams than others. Increasingly, board diversity is desired by employees, customers and other stakeholders. By obeying to this request, a company shows a sense of sensitivity towards stakeholders’ preferences, aspirations and concerns. This may result in increased customer loyalty, and employee motivation, efficiency and retention (Bilimoria, Wheeler, 2000; Powell, 1999). These stakeholder groups vary in their own levels of gender diversity. According to Brammer et al. 2007, stakeholders of institutional investors, regulatory bodies are not very likely to influence the diversity among workforces and their Top Management Teams. One of the most influential stakeholders, however, are the unions which accompany the employees working in these industries.

H3a : Industries with high union density are expected to have higher levels of gender diversity than industries

with low union participation.

H3b : Industries with high union density are expected to show a larger increase in the level of gender diversity

in TMT between 2007 - 2011

The final customer of a company’s product, however, is also a very influential stakeholder. Companies that serve high proportions of female end customers are highly likely to ensure that their workforce is a resemblance of their customer base. Industries which are part of this group, are – amongst others- goods manufacturing, retail, fast-moving consumer goods, banking, etc. In other industries, as e.g. construction and engineering, there is little contact with final customers which will, according to Brammer et al. 2007, lead to more male dominated workforces.

Table 1: Gender and ethnic diversity among workforce across sectors in the UK; withdrawn from Brammer et al. (2007)

H3c : The level of gender diversity in Top Management Teams of industries with higher proximity to the final

consumer is expected to be higher than in those of industries with relatively little contact with final consumer.

H3d : Industries with higher levels of proximity to the final consumers are expected to show a larger increase

in the level of gender diversity in Top Management Teams between 2007 - 2011

2.3.4 Non-Executives

Ethnic diversity among UK corporate boards that: “Bigger boards with larger numbers of non-executive

directorships were found to be more diverse. Consistent with findings in the existing literature (e.g. Carter et al., 2003), this suggests that the expansion in the numbers of non-executive directorships, following recent corporate governance recommendations, has facilitated growth in board diversity.” I, therefore, state:

H4a : The average number of non executive Top Management Team members within an industry is positively

related to the level of gender diversity in 2007.

H4b : The average number of non executive Top Management Team members within an industry is positively

related to the level of gender diversity in 2011.

H4c : The average number of non executive Top Management Team members within an industry is positively

related to the increase of gender diversity between 2007 and 2011.

2.3.5 Level of Technology

According to Chatman and Jehn (1994), technology is one of the most salient similarities among firms in the same industry. On the one hand, organisational culture represents how things are done (Deal, Kennedy, 1982). While on the other side, organisational technology shows how things are done by defining what is being done within the organisation. High technology firms are characterised by high expenditures on R&D. Greater similarities in the operations and processes of a firm implicate less variation in organisational cultures and a more rigid structure. Following from this reasoning, it is to be expected that industries with higher levels of technology have lower levels of gender diversity as they are less focused on their HR side as they have a more pronounced R&D procedure.

Moreover, in an article by van Beek (Betabanen.nl) is stated that, even though the percentage of female graduates is similar to the number of women that starts to work on an annual basis, their career ends in an earlier stage. Twenty years ago, only 15 per cent of all technical graduates were female (Kennislink.nl). Whereas, nowadays, this number amounts up to 40 per cent, but only few of these graduates evolve into a management function. A study by Gaia (network for career encouragement of females in the technology sector) shows that women with similar career aspirations and working hours as men do not evolve towards management positions in the same pace as men. Resulting from this, women in technical jobs leave their industries and move towards the public industries with lower technology rates, but better career opportunities. This, combined with the relatively low rates of females in technical studies, leads to the following hypotheses:

H5a : The level of technology within an industry is negatively related to the level of gender diversity in the

Top Management Teams in that industry.

H5b : Higher levels of technology within an industry will result in (a negative influence on) lower increase of

3.METHODOLOGY

The research objective of this study is to identify whether the level of gender diversity in Top Management Teams in The Netherlands differs across industries. In order to do this, I will also need to analyse whether the level of gender diversity between Top Management Teams in industries differs. And whether this can be attributed to industry characteristics. For this purpose, I will use the multiple linear regression in SPSS. The significance level for these tests is set at .05, due to the small sample used for this analysis. The alpha level is set at a higher level to ensure that relevant issues will not be wrongly discarded from this analysis.

After processing these data, results will provide two sections. Firstly, the state of gender diversity on Dutch Top Management Teams is analysed at two moments. And secondly, the variation between implementation time between the different industries will be examined.

3.1 Dependent Variable - Level of Gender Diversity

3.1.1 Level of Gender Diversity

The level of gender diversity is determined by the ratio of total number of Top Management Team members to the number of females on that Top Management Team within an industry. As previously done by Hillman, Shropshire, and Cannela (2007), I will code the dependent variable of Gender diversity as “1” if a firm’s Top Management Team of director is female and “0” if that director is male. By doing this, the level of gender diversity within a Top Management Team can be determined by taking the ratio of the total number of Top Management Team members and the number of women within that Top Management Team. The information needed will be derived from Mr. Van Veen’s database containing most data needed for the coding. Moreover, I will consult press reports and other sources on the internet to confirm that individuals are correctly coded.

3.1.2 Top Management Team

and produce significant results. This choice is justified as the two-tiered board system is mandatory by law in the country under research, i.e. The Netherlands.

3.2 Independent Variable - Type of Industry

The independent variable of this study is ‘Type of industry’.

As this research is based on Brammer et al.’s 2007 research on gender diversity in the United Kingdom, I will combine Brammer et al.’s classifications into categories which can be defined in SIC codes. This will make the classification more suitable for the sample at hand.

In order to be able to allocate the specific company to the right category, a principle business activity for each of the companies will be determined using SIC codes provided in the Top Management Project database which provides an industry classification for each firm. These SIC codes will then be categorised according to the classification provided by the United States Department of Labor’s Occupational Safety and Health Administration. Companies that did not have a SIC code in the database, were assigned to the industry category their core business is in. Companies that had core operations in multiple categories or were difficult to allocate due to any other reasons were deleted from the sample on a ‘missing values basis’. The HighBeam Business website (www.business.highbeam.com) provided background knowledge on core businesses and industry codes. This results in the following list:

1. “Retail” (i.e. Retail, Wholesale, Consumer Goods) SIC groups 50-59 2. “Mining”, (i.e. Extraction of oil and gas) SIC groups 10,12-14

3. “Banking” (i.e. Banking, Insurance, Other Finance) SIC groups 60-65, 67 4. “Transportation”, (i.e. Communications, Utilities) SIC groups 40-49 5. “Engineering” (i.e. Engineering, Construction), SIC groups 15,16,17

6. “Services” (i.e. Business Services, Hotels, public facilities) SIC groups 70, 72,73,75,76,78-84, 86-89. 7. “Manufacturing” (i.e. Chemicals, Furniture, Textile) SIC groups 20-39 .

8. “Agriculture” (i.e. Live stock, Forestry, Agriculture) SIC groups 01,02,07-09 9. “public administration” SIC groups 91-97, 99

3.2.1 Industry Variables

UNION DENSITY - Is defined as the ratio of all employees working in that industry to the employees within that industry who joined an union. For the purpose of the tests done in SPSS, this ratio is coded in 5 groups ranging from low union density to very high density. Information on Union density was derived from the CBS 2012 report on the level of organisation among employees in The Netherlands (Organisatiegraad van werknemers 1995-2011, CBS 2012). Groups were coded as follows: 0%-19.99% “1”; 20.00%-39.99% “2”; 40.00%-59.99% “3”; 60.00%-79.99% “4”; 80.00%-100% “5”. Tests were also conducted with fewer groups, however, differences between the levels of union density were relatively small and this high number of groups allows us to deliver more reliable results.

taking lower management functions is that these women have the highest potential to evolve into higher management positions and maybe eventually TMTs. Data on Gender diversity in the workforce was withdrawn from the ‘Emancipatiemonitor’ 2008, 2010, and 2012 of the CBS.

PROXIMITY TO FINAL CUSTOMER - As the final consumer of a company’s product is a very influential stakeholder, companies which serve high proportions of female end consumers are highly likely to make their workforce a resemblance of their consumer base. Industries with high proximity to final consumers (coded “1”) are therefore defined as industries in which companies regularly come into contact with final consumers. Industries with little contact with final consumers are coded “0”. Following Brammer et al. (2007), high proximity industries are – amongst others- goods manufacturing, retail, fast-moving consumer goods, banking, etc. In other industries, as e.g. construction and engineering, there is little contact with final customers which will, according to Brammer et al. 2007, lead to more male dominated workforces. To provide clarification for companies which could not be classified easily according to Brammer’s description, Datamonitors from the several industries and Marketline industry profiles were used.

LEVEL OF TECHNOLOGY - In their article on ‘Using industry classification codes to sample high-technology firms: analysis and recommendations’, Charles O. Kile and Mary E. Phillips (2009) give an overview of all industry classifications and their accuracy for sampling high-technology firms. Their analysis, combined with Francis and Schippers’ (1999) research on the accuracy of these measures and the OECD Science and Industry Scoreboards of the studied years, has resulted in the appointment of high-technology industries (coded “1”) being 8, 7, 6, and 4. I.e. Agriculture, Manufacturing, Services and Transportation, respectively.

3.3 Control Variable

In this section, I will describe variables that need to be controlled for in order to test the actual influence of industry on the gender diversity.

3.4.1 Industry Size

The average size of the companies in each industry will be controlled for. This needs to be done, in order to reassure that the effect of the independent variables can be attributed to them, and not to different factors that might be of influence. Given the fact that the companies in the database are listed at the Dutch AEX, AMX and ASMX indexes, large differences might occur in company and industry size. Data on the size of the industry’s workforce is withdrawn from the CBS 2012 report on the level of unionisation in industries in The Netherlands.

3.4.2 Top Management Team Size

3.5 Data

The data used for this study will be derived from the ‘European Top Managers Project’ database assembled by Mr van Veen. The sample will be taken from all 100 Dutch AEX, AMX and ASMX listed companies for the period from one year before the introduction of the gender quota in The Netherlands until one year after the introduction, i.e. 2007 until 2011.

4.RESULTS

Several statistical tests are conducted before drawing conclusions about the dataset. The purpose of this chapter is to present and examine the results of the quantitative data. This chapter starts with the assessment of correlation, followed by the linear regression analysis.

In the analyses, the following abbreviations will be used for the tested variables.

Gender div 07: the level of gender diversity in 2007 Gender div 11: the level of gender diversity in 2011 Level tech: level of technology within the industry Final cust: Proximity to the final customer

Gender div 06: the level of gender diversity in 2006 Gender div 10: the level of gender diversity in 2010

Non-exec 07: the average number of non-executive Top Management Team members in 2007 Non-exec 11: the average number of non-executive Top Management Team members in 2011 Union dens 07: the union density in 2007

Union dens 11: the union density in 2011

Gen div WF 07: the level of gender diversity in the workforce in 2007 Gen div WF 11: the level of gender diversity in the workforce in 2011 Total WF size 07: the total size of the workforce in 2007

Total WF size 11: the total size of the workforce in 2011 TMT size 07: main size Top Management Teams in 2007

TMT size 11: main size Top Management Teams in 2011

4.1 Assessment of correlations

Gend er div 07 Gende r div 11 Total WF size07 Total WF size11 TMT

size07 TMT size11 Level Techn Final Cust Div 06Gen Gen Div 10 Non-exec07 Non-exec11 Union Dens0 7 Union Dens 11 GenDi vWF0 7 GenDi vWF1 1 Gender div 07 Gender div 11 Total WF size07 Total WF size11 TMT size07 TMT size11 Level Techn Final Cust Gen Div 06 Gen Div 10 Non-exec07 Non-exec11 Union Dens07 Union Dens 11 GenDi vWF07 GenDi vWF11 .694 ** -.045 -.187 -.058 -.190 .987** . 514** .519** -.121 -.143 .438 ** .649 ** -.265* -.258* .630** .029 -.038 .292* .270* .176 .027 -.003 .119 -.324 ** -.211 .000 .291* -.181 .558 ** .548 ** -.032 -.052 .310* .164 -.029 .028 .770 ** .916** .-.159 -.162 .526** .577** -.053 .167 .577** .499 ** .561** -.167 -.196 .755** .765** .218 -.034 .204 .506 ** .533 ** .677 ** -.228 -.245* .612** .846** .023 .167 .229 .622** .716** .068 .068 .272* .147 .123 -.120 .230 -.775 ** .127 .010 .187 .046 .068 .068 .272* .147 .123 -.120 .227 -.775 ** .127 .011 .187 .046 1.00** -.194 -.263* .623** .726** -.255* -.199 .019 .237 -.222 -.223 -.334* -.301* -.441 ** -.441 ** -.112 -.166 .596** .715** -.191 -.137 .052 .355** -.144 -.141 -.257* -.236 -.478 ** -.478 ** .951**

Table 2 Results of bivariate correlations between constructs * = p < .05

4.2 Multiple Linear Regression Analysis

Regression analysis is a statistical technique to examine the relationships between quantitative variables. In this study, the hypotheses were formed in such a way that the regression analysis had to be performed multiple times with changing independent variables variables. The following section will go into detail concerning these analyses.

4.2.1 The level of gender diversity in Top Management Teams in 2007 and the IVs.

For the purpose of this research, I grouped the hypotheses related to the variable Number of women in Top Management Teams in 2007 together and tested them in a multiple regression in SPSS.

It is of interest to this study to determine whether each independent variable significantly contributes to explain the dependent variable. The independent variables in this test were Gender diversity in the workforce, Union density, Proximity to the final consumer, Number of non-executive board members, the Level of Gender Diversity in Top Management Teams in 2006 and the Level of technology in the industry. Table 3 shows that, when tested separately, there only is a significant relationship for the independent variable Gender diversity in TMTs in 2006, which means that Hypothesis 1a is accepted. Thus, the number of

women in Top Management Team members in 2006 is positively associated with the level of gender diversity in the Top Management Teams in 2007. The R2 _{has a value of 49.8, indicating that almost 50% per}

cent of the dependent variable is explained by the independent variables in this analysis. Table 3 further shows that none of the other variables have a significant relationship with the dependent variable. From these results can be concluded that there is no significant relation between the other IVs and the number of women in Top Management Teams in 2007.

Total WF size

07 Members 07Total Board LevelTechn FinalCust WomenTMT06 NonExec07 UnionDens07 GenDivWF07 Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 .000 .120** .001 .124*** -.160 .000 .120*** .005 .000 .088*** .503*** .000 .074** .083 .000 .120*** -.001 .000 .115*** .000 .000 .035 -.151 -.238 .515*** .106** -.484 .000

Table 3 Test of relationship between the level of gender diversity in Top Management Teams in 2007 and IVs * = p < .1

** = p < .05 *** = p < .001 D.f.: 66 N: 67

gender diversity in the Top Management Team in the year before the start of this analysis is significant at the 1% level. It can therefore be said that Hypotheses 1a, and 4a can be accepted.

This surely contradicts my assumption that the level of gender diversity in Top Management Teams in 2007 is related to the Level of technology, or the Proximity to the final customer. Also, the level of Union Density within the industry or the Gender diversity in the workforce does not have a significant relationship with the level of gender diversity in 2007. Therefore, H2a H3a H3c H5a are rejected.

4.2.2 The level of gender diversity in Top Management Teams in 2011 and the IVs

In this analysis the variables relating to the level of gender diversity in Top Management Teams in 2011 are grouped together in a multiple regression in SPSS.

Subsequently, this test determines whether each independent variable significantly contributes to explain the dependent variable. The independent variables in this test are: Gender diversity in the workforce, Union density, Proximity to the final customer, the Number of non-executive board members, the Level of Gender Diversity in 2010, and the Level of technology in the industry. For all these variables, data was gathered for the year 2011. Just like the previous analysis, this test is started with analysing the independent variables separately. Table 4 shows that there is a significant relationship for the independent variables Number of non-executive board members and the level of gender diversity in Top Management Teams in 2010, which means that Hypotheses 4b and 1b are accepted. Leading to the conclusion that the number of

non-executive Top Management Team members in 2011 is positively associated with the level of gender diversity in the Top Management Teams in 2011. Moreover, when tested separately, it seems that the number of women in Top Management Teams in the year previous to the sample year has a significant influence on the level of gender diversity in 2011 as well. Table 4 further shows that none of the other variables have a significant relationship with the dependent variable. This also further confirms that H3a, H3c, H5a are not

supported. This implies that the level of technology and the level of gender diversity in the workforce are not of influence on the level of gender diversity in Top Management Teams in 2011.

Total WF size

11 Members 11Total Board LevelTechn FinalCust WomenTMT10 NonExec11 UnionDens11 GenDivWF11 Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 .000 .120** .000 .120*** .002 .000 .130*** -.240 .000 .002 .592*** .000 -.009 .238** .000 .123*** .203 .000 .123*** .000 .000 -.038 .102 -.428* .573*** .101 -.328 .000

Table 4 Test of relationship between the level of gender diversity in Top Management Teams in 2011 and IVs * = p < .1

In model 8, the independent variables are again tested together. According to the R2 _{coefficient, our}

model explains 63.6 per cent of variance of our dependent variable. Again, the beta values in this analysis show that the recruitment base from which the new Top Management Team members are derived has proven to be a solid predictor of the level of gender diversity in Top Management Teams in 2011. Furthermore, it seems that companies do see their Top Management Teams as a proper reflection of their customer base. And therefore, the final customer seems a powerful stakeholder of the companies and a determinant of the level of gender diversity in Top Management Teams in 2011.

4.2.3 The increase in the level of gender diversity ‘07 -’11 and the IVs

In this analysis the variables relating to the level of gender diversity in Top Management Teams between 2007-2011 are grouped together in a multiple regression in SPSS.

Subsequently, this test determines whether each independent variable significantly contributes to explain the dependent variable which is for this analysis defined as the difference between the level of gender diversity in 2007 and 2011. The independent variables in this test were, Difference in gender diversity in the workforce between 2007-2011, Difference in union density, Proximity to the final consumer, Difference in the number of non-executive board members, the level of gender diversity in the Top Management Teams in the years before 2007 and 2011, and the Level of technology in the industry. Table 5 shows that in this analysis, no significant relationships are to be found and that none of the hypotheses relating to the increase in the level of gender diversity between 2007 and 2011 are supported by this analysis. The explanatory power of this analysis, however, is only limited, with a R2 _{of only 24,3%.}

Total WF size Total Board

Members LevelTechn FinalCust WomenTMT06-10 NonExec07-11 UnionDens07-11 GenDivWF07-11 Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 .000 .087** .000 .089** -.137 .000 .085** .065 .000 .058* .179 .000 .093** .051 .000 .089** .120 .000 .085** .000 .000 .057 -.108 .345 .169 .041 .438 .000

Table 5 Test of relationship between the increase in the level of gender diversity in Top Management Teams between 2007-2011 and IVs

* = p < .1 ** = p < .05 *** = p < .001 D.f.: 66 N: 67

5.CONCLUSION&DISCUSSION

The goal of this research is to find out whether the level of gender diversity in Top Management Teams in The Netherlands differs across industries, and to identify possible differences in the increase in the number of women in Top Management Teams between 2007 an 2011 between industries. This chapter answers the research question by discussing the results of the analyses, and the theoretical and managerial implications of the results. Moreover, it covers the limitations of this study and the suggestions for future research and it ends by presenting a final conclusion.

5.1 Discussion of results

This study investigated whether the level of gender diversity in Top Management Teams differs across industries in The Netherlands, and whether the increase in the level of gender diversity can be attributed to the industry a company operates in. In order to do this, a sample of 67 companies was analysed. For this analysis, five industry characteristics were identified and their influence on gender diversity was measured.

Figure 1: Overview of development of the level of gender diversity in Top Management Teams over the years 2007-2011.

In figure 1, it can be seen that there was only a limited increase in the number of women between the years 2007 and 2011. It does however show that there is a considerable difference between the level of gender diversity in different industries. In this study, it is researched if this difference can be attributed to the industry the company operates in. From the multiple regression that was performed on the dataset, it can be seen that not all defined industry characteristics are supported by the statistical analysis. Figure 1 indeed shows a slight increase in the number of women across industries. The industries “3” Banking and “4”

0 5 10 15 20 25 30 2007 2008 2009 2010 2011

Retail Banking Transportation Engineering

Transportation show the largest increase, with 7 and 6 women, respectively, added to the Top Management Teams over the years 2007-2011. What can also be seen, however, is the fact that in industry “7” Manufacturing there already were more women in the Top Management Teams to begin with. The finding of these industries banking, and Transportation partly confirm the claims of Chung, Kerkhofs, and Ester (2007), and Flynn and Adams (2004) who claim that public utilities, financial, and other services sectors have the highest levels of gender diversity in their workforces and higher management.

There is no effect explained for the different stakeholders (unions and final consumers) that were subject of the research on the separate years. This also goes for the development in the number of women in Top Management Teams over the years 2007-2011, however, the results of this analysis show that there is indeed a significant positive relation between the proximity to the final customer and the level of gender diversity in Top Management Teams in 2011.

Moreover, the results demonstrate that the level of technology does not impact the level of gender diversity in Top Management Teams. In contrast to expectations and the findings of Charles O. Kile and Mary E. Phillips (2009), level of technology within an industry appears not to be associated with the level of gender diversity in Top Management Teams in The Netherlands. Reason for this, might be that other factors, like e.g. educational level of the (female) employees, are more important.

According to this analysis, the number of women already appointed on Top Management Teams in previous years has a positive and significant relation on the level of gender diversity in Top Management Teams. These results, however, might be biased as the employment term of directors in Top Management Teams most of the time exceeds the period on which this analysis is conducted.

As stated previously in this research, larger, and more diverse non-executive boards result in higher levels of diversity in Top Management Teams (Brammer et al. 2007). The result of this analysis confirms this statement, and shows that the number of non-executives indeed has a positive and significant relationship with the dependent variable the level of gender diversity in Top Management Teams.

5.2 Limitations and further research

Brammer et al (2007) conducted a similar research, but only for UK firms. Their paper can therefore be regarded as the starting point of my research. My paper contributes to previous studies in this area by expanding the research field to Dutch companies and industries.

The analysis of gender diversity in Dutch Top Management Teams across industries depends on much more than the studied aspects. Other variables were not included in this study as it was beyond the scope of this research and has to be a topic included in further research. For further research, I would therefore recommend to include more variables. I suggest expanding on this research by adding several other industry characteristics like e.g. educational level, or the level of nationality diversity within an industry, which might provide a better ground to compare the industries on.

I strongly suggest to re-test these hypotheses with other, more complete, databases in order to have comparable results and critically handle the results found. By using a more extensive database, the other industries (2,8,9) can also be included for a more complete result.

most probably be inconsistencies in data collection. For the purpose of this study, I used the best option within my reach, but for the purpose of further research one could decide to make a distinction between executive and non-executive members or to exclude either one of them from research. This might be particularly interesting in comparison with other countries. This issue needs to be kept in mind while interpreting the results.

Furthermore, it would benefit the result of the research if the study would be conducted in a more longitudinal way. Including more years into the study would provide a better overview and show patterns more clearly. Those patterns would make it possible to analyse the development of the different coherences over a longer time span.

Regarding the dimension on the level of union density, results might also be biased, as industries with higher levels of union density tend to be more conservative, which might have its own effect on the level of women in the workforce and on the Top Management Teams.

Finally, one could choose to use a different, more specific, classification for the Level of Technology and Union Density. As differences between companies and industries were really small, a more specific and detailed classification with e.g. smaller steps would probably yield better results. Also, one could make use of another industry classification. Even though all classifications have their flaws and limitations, the SIC classification used in this research might, for some industry characteristics (e.g. Level of technology) be somewhat outdated.

5.3 Conclusion

This research was guided by the following research question: “Can the level of gender diversity in Top Management Teams in The Netherlands be attributed to the industry a company operates in, and if so, which industry characteristics are of influence on the level of gender diversity in Top Management Teams?” I tried answering that question by identifying the relevant industry characteristics and there influence on the level of gender diversity in Top Management Teams in The Netherlands. With these significant industry characteristics, industry scores on these characteristics can be measured and compared. Industries which score higher on the significant industry characteristics are expected to have higher increase in the number of women in their Top Management Teams.

1 2 3 4 5 6 7

Recruitment Base 0 X 1 0 0 1 1

Final Customer 1 X 1 0 0 1 0

Non-Executives 0 X 1 0 1 0 1

Table 6 Summary of industry scores on significant dimensions “X” No data available, industry not analysed

“0” Low “1” High

industry characteristics that have proven to be significant in my analysis are coded “1”, and industries which score relatively low on that particular dimension score “0”.

The results of my analysis suggest that companies within industries with high levels of gender diversity in the Top Management Teams in previous years (i.e. in their recruitment base), with gender diversity in the non-executive Top Management Teams, and with a closer proximity to the final customer have a larger increase in the number of women in their Top Management Teams. This corresponds with the conclusion of Harzing, Myloni, and Mirza (2004), which says that companies tend to align their human resource strategies to the industry they operate in. As can be seen in table 6, the industries which score high on most of these dimensions are industries “3” Banking, “ 5” Engineering, and “7” Manufacturing. This implies that these industries have higher levels of gender diversity in Top Management Teams than others. And that the industry type does indeed have an influence on the level of gender diversity within companies. There are, however, many other factors that might influence this. These are beyond the scope of this analysis, but might be relevant for future research.

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