The Influence of Board Gender Diversity on R&D Expenses in the
European Union
July 6th, 2014 Pleuni Vreeswijk Hagestraat 1a 2011CT Haarlem 06 425 14 619 pleunivreeswijk@hotmail.com s1724428 University of GroningenMaster Business Administration
Specialization Organizational and Management Control Supervisors:
Dr. B. Crom Dr. T.A. Marra
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Abstract
In this paper the influence of board gender diversity on R&D expenditures is examined. Board gender diversity is a topic which is currently under heavy debate in the European Union and her member states. R&D expenditures are an important input to the innovation process and can lead to greater firm performance. Using a sample of 322 listed companies in the European Union, a significant positive relationship is found between board gender diversity and R&D expenditures. However, executing additional tests, the positive relationship only remains robust for Anglo-Saxon countries. In Continental Europe countries I find no relationship between board gender diversity and R&D expenditures. This paper adds to the existing theory that board gender diversity has a significant positive effect on R&D expenditures in Anglo-Saxon countries, while there is no effect in Continental-Europe countries. This difference can be explained from the differences between the corporate governance systems which are in place in the countries. The managerial conclusion is that the European Commission needs to stimulate board gender diversity in Great-Britain and Ireland, because in these countries a more gender diverse board has a significant positive effect on R&D expenditures. However, for other countries of the European Union, the European Commission needs to find other ways to stimulate R&D spending, because board gender diversity has no effect on R&D expenditures in these countries. In this search of other ways, the influence of the different corporate governance systems of the member states needs to be kept in mind.
JEL Classification: J16, M14, O31
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Table of Contents
1. Introduction ... 3
1.1 Purpose of the paper ... 4
1.2 Managerial and theoretical relevancy ... 5
1.3 Outline of the paper ... 7
2. Theoretical Framework ... 8
2.1 Reasons for the current debate about women quota ... 8
2.1.1 Theories on board gender diversity ... 8
2.1.2 The differences in characteristics between men and women on board level and between average men and women ... 10
2.2 R&D, innovation and firm performance ... 12
2.3 Hypothesis development ... 15
3. Methodology ... 17
3.1 Justification of literature research ... 17
3.2 Data collection... 17 3.3 Measures ... 18 3.3.1 Independent variable ... 20 3.3.2 Dependent variable... 21 3.3.3 Control variables ... 22 3.4 Sample description ... 25 3.4.1. Missing variables ... 25 3.4.2. Outliers... 25 3.4.3. Descriptive statistics ... 26 3.4.4 Correlations ... 30 3.5 Methodology ... 32 4. Results ... 34 4.1 General analysis ... 34 4.2 In-depth analysis ... 37
5. Discussion and Conclusion ... 39
5.1 Discussion ... 39
5.2 Conclusion ... 41
5.3 Limitations and suggestions for further research ... 42
References ... 44
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1. Introduction
Recently there has been much debate in the western economies about getting more women on top of organizations. All kinds of measures are taken to breach the renowned “glass ceiling”. Some countries have even legally established quota for women in top of organizations. Norway for example, enacted a law in 2003 requiring firms to increase the share of female directors to 40% by 2015 (Adams and Funk, 2012). Spain passed guidelines in 2007 to encourage firms to increase the share of female directors to 40% by 2015. In France, the government proposed a law that will impose 20% gender quotas on boards of listed firms within three years of the law’s adoption and 40% quotas after six years (Adams and Funk, 2012). Similar laws are currently under debate in the Netherlands, Belgium and Germany. In the European Union, there is debate whether or not to impose such a quota on the whole European Union. The European Commission wants 40% of the top positions in listed companies to be filled by women from 2020 on. Why do governments put so much effort in getting more women in top positions? What is the effect of a woman in top of the organization? Do women really run a company different than men do? Understanding whether corporate outcomes can be expected to change with more women in top business positions is particularly important in light of this increasing worldwide trend to enact boardroom gender quotas (Adams and Funk, 2012).
4 to see if the striving of the European Commission to get 40% of the top positions filled by women is contingent with their other aim, becoming the most competitive knowledge based economy in the world.
1.1 Purpose of the paper
The purpose of this paper is to see if the two goals of the European Union are contingent with each other. To do that, the effect of board gender diversity on the R&D expenditures will be investigated. R&D is a long term investment with uncertain returns and it can cause big profits or losses. It therefore takes a considerable amount of risk taking to invest in R&D. It has been investigated that men possess more risk appetite than women so there should be differences noted for this particular subject. The exact research question investigated in this paper is: What is the effect of board gender diversity on a company’s R&D expenditures?
5 Investing in R&D is important for three reasons. The first one is that R&D is an important input to the innovation process (Barker and Mueller, 2002). Innovative activity is risky, but its successful outcome confers monopoly power on the innovator. Successful R&D can create new products and services or improve the quality of the products and services, which may work as barriers to entry, as intangible capital stocks, or as market demand factors that bring positive values to a firm’s performance and future growth opportunities (Zhu and Huang, 2012). The second reason why investing in R&D is important is from a strategic perspective. An important output of R&D is new knowledge and information. In the emerging learning economy, the ability to generate, learn and share ideas and knowledge has been considered as a critical source of competitive advantage (Zhu and Huang, 2012). Third, following Treacy and Wiersema (1993), R&D can help organizations to deliver superior customer value. This can be done through the strategy of operational excellence, customer intimacy, or product leadership. The strategy of product leadership is evidently related to an organization’s R&D capability. Investing in R&D will increase the R&D capability, which will lead to product leadership. Therefore, investing in R&D is important to reach superior customer value (Krasnikov, 2008). For these three reasons, understanding if more women on the board are associated with higher or lower levels of this important input advances the knowledge of innovative processes.
1.2 Managerial and theoretical relevancy
From a managerial perspective the outcomes of this study can be useful for future goal setting in the European Union, and will provide insights if their current goals can simultaneously be accomplished. Furthermore, companies who pursue a product leadership strategy can also benefit from the outcomes.
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1.3 Outline of the paper
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2. Theoretical Framework
2.1 Reasons for the current debate about women quota
In the light of recent corporate scandals and the ongoing financial crisis, the question has been raised whether things would be different if more women ran the corporate world and the financial sector. This call for a more feminine touch in the corporate world has two reasons. One of them is that companies have been too male dominated and oriented, and as a logical outcome, the culture and norms and values within the companies are as well. This has led to risk taking and a short term focus. Another reason is the fact that when a company is male dominated, the employees are homogenous. Bringing in more women to the workforce ensures more diversity and diversity can have several benefits for an organization. In the next section of this paragraph the benefits of board gender diversity and several outcomes of research on board gender diversity will be discussed. The following section will discuss the differences in characteristics between men and women.
2.1.1 Theories on board gender diversity
9 variables such as age, race and gender”. A gender diverse board may also generate a better public image of the firm and improve firm performance (Shukeri et al., 2012). Also, Adams and Funk (2012) argue that diversity may be particularly valuable in difficult economic times. Furthermore, investors value diversity. For example, Carter et al. (2003) and Adams and Ferreira (2003) find a positive relation between the percentage of women on the board of directors and firm value as measured by Tobin’s Q. Erhardt et al. (2003) find evidence of a positive relation between percentage of women and return on assets and return on investment. These advantages of board gender diversity are summed up in table 1. So if women add new perspectives that are value enhancing, they will become more prevalent on boards and will be associated with enhanced shareholder value (Farrell and Hersch 2005). This implies a positive relation between performance and the number of women on the board (Farrell and Hersch, 2005).
Table 1. The advantages of board gender diversity
• People with different backgrounds may have different viewpoints, which can lead to better decision making and more effective problem solving.
• When the market place is diverse, a more diverse board could perhaps better understand this marketplace.
• Attitudes, cognitive functioning and beliefs are not randomly distributed in the population, but tend to vary systematically with demographic variables such as age, race and gender. Diversity increases creativity and innovation.
• A gender diverse board may also generate a better public image of the firm.
• Investors value diversity resulting in a higher firm value, measured by Tobin's Q, ROA and ROI.
10 88 companies with the highest representation of women on their top management teams experienced significantly higher returns on equity and total return to shareholders when compared to the 89 companies with the lowest women’s representation. These results maintain robust after controlling for industry factors (Farrell and Hersch, 2005).
In contrast, other academic research provides evidence that board gender diversity can result in poorer firm performance (Adams and Ferreira, 2009). Even when positive relationships are found between board gender diversity and firm performance; it is unclear if diversity led to performance or vice versa (Hamdani and Buckley, 2011). Thus, it is difficult to argue that gender diversity on boards automatically produces higher performance (Fitzsimmons, 2012). Fitzsimmons (2012) argues that it is not enough to just place a couple of women in the board of directors, there needs to be a proper environment first that allows gender diverse boards to flourish.
To conclude, a complex relationship exists between board gender diversity and organizational performance whereby it has been found to have negative (Adams and Ferreira, 2009), positive (Roberson and Park, 2004), and neutral (Farrell and Hersch, 2005) impacts. The reason for these contradicting results is perhaps, as previously mentioned, because the influence of board gender diversity on firm performance is indirect, instead of direct. However, the reason of the debate about women quota is because the corporate world is male dominated and therefore there is much risk taking and a short term focus. In the next section I will investigate the differences in characteristics of men and women.
2.1.2 The differences in characteristics between men and women on board level and between average men and women
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Table 2. The differences between men and women on board level and on average level Men Women in boards Average women Care about Competition Benevolence Tradition
Conformity Universalism Security
Stimulation Stimulation
Care less about Tradition Power Competition Security Conformity
Conformity Stimulation
Tradition
Risk appetite
+ -
+
- -
Adler (2002) stated why women’s approaches to managing bring more value to organizations. He calls this the feminine advantage. Women have greater tendency to use more democratic, inclusive, participative, interactional and relational styles of managing. Adler (2002) quotes the anthropologist Fisher in his conclusion that women have many exceptional faculties for managing internationally, including: “a broad contextual view of any issue, a penchant for long-term planning, a gift for networking and negotiating . .. a preference for cooperating, reaching consensus, and leading via egalitarian teams .. . an ability to do and think several things simultaneously . .. emotional sensitivity . .. and a talent with words” (Fisher, 1999: xvii). Especially the tendency for long term planning is an important incentive to invest in R&D.
So on board level and on average, female directors differ significantly in their norms and values. Furthermore, female managers have a different management style than men do.
2.2 R&D, innovation and firm performance
In the introduction the importance of investing in R&D is briefly mentioned. This section elaborated on the different theories on R&D.
14 significant R&D expenditures will have large financial performance. Taken all of the above together, research has found that R&D has a positive influence on sales, firm growth, market share, market value and ROA.
Zhu and Huang (2012) also explained another reason why investing in R&D is so important. They quoted D’Aveni et al. (1995) by stating that business environments are currently in a state of “hyper-competition”. Firms must continuously develop themselves in new directions which are based on rapid technology and knowledge-, creation, acquirement, diffusion and use to keep a sustainable competitive advantage. These things are dependent on the firm’s investment in R&D. The key competitive success factor is the ability to constantly develop new products, processes or services providing the customer with increased functionality and performance, which can be reached by investing in R&D. And those success factors are followed by a positive financial performance. To conclude, investing in R&D is one of the key drivers of a competitive advantage.
Table 3. The importance of investing in R&D
• R&D is an important input to the innovation process. Successful R&D can create new products and services or improve the quality of the products and services which bring positive values to a firm’s performance and future growth opportunities.
• There are important outputs of the innovation process such as knowledge and innovation. The ability to generate, learn and share ideas and knowledge has been considered as a critical source of competitive advantage.
•Investing in R&D is a strategy to deliver superior customer value through product leadership.
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2.3 Hypothesis development
Taken all of the above theory together, researchers believe that women on corporate boards have different values and management styles than men do. Women have a long term focus, are slightly more risk loving, and attach less value to security and tradition. These attributes are important incentives to invest in R&D. After all, the outcomes of R&D are only visible in the long term, investing in R&D can be risky due to the large amounts of money R&D is concerned with and your innovation can be easily copied by other organizations. Because of this risky invest, the position of a (female) director is on the line when investing in R&D. However, women care less about security so they should be more inclined than men to invest in R&D. Also, the fact that they attach less value to tradition makes them more inclined to invest in R&D. Tradition is about doing things the same every time and about holding on to the old way. R&D leads to innovation. When processes are innovated they become different. Tradition oriented people are not likely to invest in R&D.
16 such, the information from females can be a pivotal source of information which supports innovative activity (Miller and Triana, 2009).
The combination between men and women in the board however is the key to better decision making and more effective problem solving. People with different backgrounds may have different viewpoints (Adams and Funk, 2012). The variety of viewpoints causes decision makers to evaluate more alternatives. After considering all the possible options the most optimal one can be chosen. Therefore, decisions made by a gender diverse board are qualitatively better. Taken all of the above together, I come to the following hypothesis:
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3. Methodology
In this section the methodology used to assess the hypothesis will be explained. First a justification of the literature research is presented. The following section will discuss the data collection. In the third section the measures will be explained and also the used variables will be presented. Hereafter a description of the used sample will be presented. This chapter ends with the presentation of the linear regression model which will be used to estimate the regression coefficients.
3.1 Justification of literature research
The theoretical framework is based on previous literature. This literature was found mostly in Business Source Premier, a database containing journals in the field of economics and business administration. Only peer reviewed articles were searched on. Used search terms are; board gender diversity, gender diversity, board of directors, R&D, innovation, female characteristics. In the relevant yielded papers references to other relevant papers were found. Most of these papers were also found in Business Source Premier. When the papers however weren’t available in Business Source Premier, Google Scholar was used to find these papers. Furthermore, to be sure no other relevant papers are missed, the same search terms as used in Business Source Premier are looked upon in Google Scholar. This yielded some more relevant papers.
3.2 Data collection
18 DataStream. DataStream is a database containing information of especially large capitalization companies. It is updated daily.
Similar to Carter et al. (2003), Erhardt et al. (2003) and Miller and Triana (2009) the focus of this paper is a one year horizon, namely 2012, which in my opinion will not affect the relevance and accuracy of the conclusions which are drawn from the analysis. In 2012 there were 27 member states of the European Union so I only used data from these countries. The search within Orbis consisted of several steps because the sample needed to be filtered on several criteria. At first, the sample is filtered on industrial companies, leaving financial and insurance companies out. Furthermore, there needed to be data available about R&D expenses. Publicly traded companies are obliged by the IFRS to include R&D expenses in their annual report. From DataStream the data regarding the board of directors was collected. Hereafter the two datasets were merged together using Microsoft Excel. This search process resulted in a sample of 322 firms, and this sample is used to investigate the relation between board gender diversity and R&D expenditures.
3.3 Measures
In this section the variables will be discussed. First, a thorough investigation of previous research has been done. In table 4, an overview is given of the used operationalizations in previous literature for the dependent and independent variable used in this paper. Furthermore, the last column shows relevant control variables. Based on this literature research, several control variables are chosen. The argumentation for these chosen control variables will be presented in the corresponding section.
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Table 4. Overview of operationalizations used by other authors
Author and year Board gender diversity R&D expenses Control variables
Barker and Mueller
(2002 NA
Total R&D dollars spent per employee per firm relative to the firm's industry average**
Institutional stock holdings - Past performance (return on assets lagged by one year) - Firm size (natural log employees) - Related and unrelated diversification - Leverage
Baysinger, Kosnik
and Turk (1991) NA
Corporate R&D spending per employee averaged over a 4 year period**
Average industry R&D intensity (weighted average of industry R&D spending in all industries in which a firm operated) - Diversification - Firm size (sales)
Bear, Rahman and Post (2012)
Number of women in the
board of directors* NA NA
Carter, Simkins and Simpson (2003)
A dummy variable coded as one if there is at least one female member of the board of directors, and the percentage of women on the board*
NA NA
Erhardt, Werbel and Shrader (2003)
The percentage of the women in board of directors, averaged over a 2 year period*
NA NA
Ettlie (1998) NA
R&D intensity, measured by reported investments in R&D per sales dollar*
NA
Gallasoe and Simcoe
(2011) NA
R&D expenditures as a measure (amongst others) of innovation**
NA
Hillman, Shropshire and Cannella (2007)
A dummy variable coded 1 if a firm’s board of directors included at least one woman and 0 otherwise**
NA NA
Mateos de Cabo, Gimeno and Nieto (2012)
The proportion of female
directors on the board** NA NA
Miller and Triana (2009)
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liquidity (current ratio) - Industry
Sher and Yang
(2005) NA
R&D intensity (as a proxy for innovation) is measured as the ratio of R&D expenditure to a firm’s total number of employees*
NA
Shukeri, Shin and Shaari (2012)
The proportion of female directors to the total number of directors*
NA NA
Torchia, Calabro and Huse (2011)
A dummy variable for 0, 1, 2, or at least 3 women in the board of directors*
NA
Firm size (ln employees) - Industry type (dummy for high tech)
Zhu and Huang
(2012) NA
R&D expenditures divided by revenue and R&D expenditures divided by assets*
NA
Note: * Variable is used as independent variable. ** Variable is used as dependent variable.
3.3.1 Independent variable
21 more easily collected. The data are approximately normally distributed, which is a necessary assumption to perform statistical tests and base assumptions on those tests.
To perform a robustness check on the results there is also another operationalization of board gender diversity chosen. This operationalization is based on Carter et al. (2003) and Hillman et al. (2007). In both these papers a dummy is used for board gender diversity, which was coded 1 when there was at least one woman in the board of directors and 0 if the board consisted only of men. The disadvantage of this variable is that it does not say anything about the degree of diversity. When there are 10 board seats, and only one seat is occupied by a woman, is it possible to speak of a gender diverse board? Perfect board gender diversity means a 50/50 distribution of men and women, and in this case the proportion is only 10%. However, this operationalization will only be used as a check on the results.
3.3.2 Dependent variable
22 Other papers in table 4 use only R&D expenditures or divide it by total assets (Zhu and Huang, 2012) or the number of employees (Baysinger et al, 1991; Sher and Yang, 2005). There are several reasons not to choose for these operationalizations. First of all, the relation investigated in this paper, has been investigated by Miller and Triana (2009) in the US. They found a significant and positive relationship. Therefore, in this paper the same operationalizations are used. Second, the total number of employees is used to control for firm size and total assets is very industry sensitive. For these reasons there is chosen to divide by firm’s net operating revenue.
Companies are obliged to state their R&D expenses in their income statement. When there was no value available, I therefore assumed it was negligible. Following previous research, missing R&D values were set equal to zero (O’Brien, 2003; Opler et al., 1999). This method avoids biasing results by excluding firms with small R&D expenditures (Miller and Triana, 2009; O’Brien, 2003).
R&D intensity data are relatively normally distributed.
3.3.3 Control variables
23 industry. It is necessary to control for firm size because there has been argued that R&D expenditures systematically associate with firm size (Barker and Mueller, 2002). Following Barker and Mueller (2002), Miller and Triana (2009) and Torchia et al. (2011), firm size is operationalized by the number of employees. Using this operationalization instead of the also commonly used firm sales is because previous research has found that the number of employees is more stable and less sensitive to the spurious effects of business cycles, accounting manipulations, and asset sales (Baysinger et al., 1991). Looking at the histogram of the number of employees, the distribution is not normal. Non-normal distributions of variables could lead to biased and skewed results. Therefore a natural logarithmic transformation of the number of employees is included. These data show an almost perfect normal distribution.
The next control variable, firm liquidity is measured as current assets minus stock, divided by current liabilities. The ability to make R&D investments requires financial resources (Baysinger and Hoskisson, 1989). Therefore, liquid firms have a greater opportunity to invest heavily in R&D than companies with little financial slack, wherefore needs to be controlled. The data show an approximately normal distribution. This variable is in the paper of Miller and Triana (2009) significantly and positively related to R&D intensity (β = ,016, p < ,001).
24 sample are in the Manufacturing industry and the average R&D spending is in the Manufacturing industry the highest compared to the other industries.
The industry codes used in this paper are the NACE Rev 2 codes1. This economical classification of the activities of companies is set by the European Union and therefore more appropriate than the commonly used US based SIC codes. The classification is based on the activity which brings the company the most added value. In this paper only the big economical sector are used, which are denoted by a capital letter, for example industry J – Information and communication.
The last control variable added is past performance. This variable was not used by Miller and Triana (2009), but by Barker and Mueller (2002). They argued that firms tend to reduce their R&D expenditures when they are unprofitable. Indeed, they find a significant positive relation between past performance and R&D intensity (β = ,10 , p < ,10). However, Hitt et al. (1991) found a significant negative association between past performance and R&D expenditures, thereby arguing that unprofitable organizations are motivated to experiment with innovative activity. Taken this together, past performance influences a firm’s R&D expenditures one way or another. Therefore, past performance is added as a control variable. Past performance is measured as the return on assets (ROA) before tax in 2011. The data on R&D are of 2012. Following the previous arguing, R&D expenditures follows firm performance. Therefore firm performance data of 2011 are taken. The data regarding ROA are relatively normally distributed.
Not all the mentioned control variables of table 4 are used. This is because some variables turned out not to be significant in the corresponding models or because the literature was inconclusive about the effect of the variables on R&D expenditures. It is not the aim of this paper to investigate the relationship of these variables on R&D.
1
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3.4 Sample description
3.4.1. Missing variables
Analyzing the data, there are a few missing variables which needs to be mentioned. From three companies there was no data available on the number of employees (firm size). On the data on ROA there was one missing variable. These few missing variables do not have a large impact on the results, considering the sample size. In table 5 this is schematically presented.
Table 5. Overview of missing variables and removed outliers per variable Missing variables Outliers
R&D intensity - 5
Firm size (number of employees) 3 2
Liquidity ratio - 3
ROA before tax 2011 1 3
Total 4 13
3.4.2. Outliers
26 paper there is chosen not to replace the outliers with a maximum value, in order to be able to generalize the findings as much as possible and to manipulate the data as little as possible. This last consideration is mainly based on recent major data manipulation scandals caused by several scientists. This outlier treatment method is based on Opler et al. (1999), in this paper the 1% largest and smallest R&D observations are deleted. To check what the consequences of the removal of the outliers are, the raw data are also examined using analysis of variance. The results are practically the same as the results from the data without the outliers (F(14, 304) = 23.40, p < .001). In table 5 the number of removed outliers per variable is presented. In total 13 outliers are removed from the dataset.
3.4.3. Descriptive statistics
In table 6 the mean, median, minimum value, maximum value, the standard deviation and the number of observations per variable are presented. The reason that not all observations count up to 322 is because of missing variables and deleted outliers, as discussed in the previous sections. Because R&D intensity and firm size are transformed variables, these numbers do not say much. What is interesting to see is that the maximum value of board gender diversity 45% is. This means that in not a single board in the sample there are more women than men.
Table 6. Descriptive statistics of the variables
Mean Median Minimum Maximum Stand. Dev. Observations
R&D Intensity -0,14 -0,04 -1,54 0,16 0,25 317 Board Gender Diversity 16,03 15,38 0,00 45,45 10,46 322 Firm Size 9,70 9,59 5,20 13,38 1,45 317 Liquidity Ratio 1,10 0,97 0,21 4,05 0,58 319 Past Performance 7,92 7,14 -16,76 30,38 7,02 318
Note: for measures, see paragraph 3.3
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Table 7. Board gender diversity in the sample countries Number of companies Av. Women (%) Firms with no women on the board (%) Firms with one women on the board (%) Firms with two women on the board (%) Firms with three or more women on the board (%) AT 10 9 40 10 30 20 BE 9 11 22 33 33 11 DE 48 14 13 27 25 35 DK 10 15 10 30 50 10 ES 18 15 6 33 33 28 FI 20 27 0 30 35 35 FR 44 23 2 9 20 68 GB 92 14 22 38 29 11 HR 1 27 0 0 0 100 HU 1 6 0 100 0 0 IE 6 9 0 83 17 0 IT 12 7 42 33 17 8 LU 5 10 60 0 20 20 NL 16 15 25 38 31 6 PL 1 15 0 0 100 0 PT 4 5 25 50 25 0 RO 1 9 0 100 0 0 SE 24 23 0 21 33 46 Total* 322 16 14,9 29,5 28,3 27,3
Note: Meaning of abbreviations: AT = Austria, BE = Belgium, DE = Germany, DK = Denmark, ES = Spain, FI = Finland, FR = France, GB = Great-Britain, HR = Croatia, HU = Hungary, IE = Ireland, IT = Italy, LU = Luxembourg, NL = The Netherlands, PL = Poland, PT = Portugal, RO = Romania, SE = Sweden.* Total refers in the first column to the total number of companies in the sample, in the second column to the total number of females in the sample divided by the total number of board seats in the sample (the sample mean) and in the third to sixth column to the percentage of the 322 companies with respectively zero, one, two or three or more women in the boards of the sample.
29 are having their main activity in this industry. From the Arts, Entertainment and Recreation industry there are only two companies in the sample. This distribution of the companies among the industries is probably because Manufacturing companies are more often publicly traded, and Arts companies for example aren’t. Because there is controlled for industry using dummies it does not matter that the distribution among the companies is skewed. Furthermore, this paper does not want to investigate the R&D expenditures between industries but in Europe as a whole.
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Table 8. Breakdown of the industries in the sample
Industry Average R&D spending per company (x 1000 Euro) Percentage of women in the board (%) Number of companies in the sample Number of companies with no women in the board Number of companies with one women in the board Number of companies with two women in the board Number of companies with three or more women in the board
B - Mining and quarrying
314,201 15 17 3 4 6 4
C - Manufacturing
429,307 16 204 31 62 58 53
D - Electricity, gas, steam and air conditioning supply 88,476 19 16 3 0 4 9 E - Water supply; sewerage, waste management and remediation activities 36,710 19 5 0 2 1 2 F - Construction 24,056 15 12 2 5 3 2
G - Wholesale and retail trade; repair of motor vehicles and motorcycles
33,635 14 7 1 2 3 1 H - Transportation and storage 29,624 13 8 2 1 4 1 J - Information and communication 315,162 19 33 4 8 8 13 M - Professional, scientific and technical activities
103,908 15 12 2 5 3 2
N - Administrative and support service activities
172,936 14 6 0 5 0 1
R - Arts, entertainment and recreation
66,541 19 2 0 1 1 0
3.4.4 Correlations
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Table 9. Correlation matrix including significance levels
R&D intensity Percentage of females Firm size Liquidity Ratio ROA 2011 R&D intensity - Percentage of females -,261** - Firm size ,624** ,163** - Liquidity Ratio -,250** -,186** -,309** - ROA 2011 -,261** -0,021 -,136* ,181** -
Notes:** = Correlation is significant at 1%, * = Correlation is significant at 5%.
All but one correlation are significant. This is due to the relative large sample size. Noteworthy is the fact that there is a significant small negative correlation between R&D intensity and percentage of females, while in this paper it is argued that when there are relatively more women on the board, R&D expenditures are also relatively high. Furthermore, there is a significant strong relation between firm size and R&D intensity. This is because larger firms have more capacity to invest in R&D and the reason that firm size is a control variable in this paper. The small negative relationship between liquidity ratio and R&D intensity is also interesting; a reason for this can be that the liquidity ratio and the R&D expenditures are both numbers from 2012. When firms spend money on R&D in a year, that money is not readily available to pay off short term debts and therefore the liquidity ratio is lower for these firms. Lastly, the small negative relationship between ROA 2011 and R&D intensity is a first indication of the direction of the relationship. This supports Hitt et al. (1991), who found a significant negative association between past performance and R&D expenditures, arguing that unprofitable organizations are motivated to experiment with innovative activity. However, the regression analysis should decide whether the significant correlation will lead to a significant result when estimating the regression formula.
32 multicollinearity, because the correlation should be higher then. However, to be sure multicollinearity does not distort the results, a check will be performed in the next chapter.
3.5 Methodology
Quantitative data analysis is used to assess if the hypothesis is supported. This method, as opposed to qualitative methods, is more appropriate to empirically test the hypothesis because of the unclear outcome of it. Currently, the literature about board gender diversity and the effect of it on R&D is vague and divergent. Therefore, quantitative methods are appropriate. Furthermore, quantitative methods are more objective than qualitative methods because of the use of hard data. Therefore, conclusions which can be drawn from quantitative methods are more appropriate to generalize across the entire population. When these conclusions are drawn, qualitative methods are a good way to investigate the reasons behind the conclusions.
In this paper, the basis of the analysis will be ordinary least squares (OLS). A simple linear model with only one independent variable looks like:
i i
i X
Y
where y represents the dependent variable. The subscript I (i=1,2,…N) indicates the unique entities. Furthermore, α is the constant, X is the independent variable whereas β is the coefficient of the independent variable, and μ is the independent error term. The particular multiple linear regression model (2) that will be used in this research is:
i ij N i 4 i 3 i 2 i 1 i
i α β*BGD β *FIRMISIZE β *LIQ β *FIRMPERF β *DUMMYINDUSTRY μ
RD
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4. Results
4.1 General analysis
In this section, the results of the regression analysis will be presented. Using the model (2) presented in the previous section, the regression estimates should explain whether or not board gender diversity is a good predictor of R&D intensity. Furthermore, the tests will also show if the control variables have a significant influence on R&D intensity.
35 The regression estimates are also shown in table 10. The regression estimate of percentage of females is β = ,003 p < ,001. This means there is a significant positive relationship between board gender diversity and R&D intensity. This means that if the percentage of females increases with 10%, a firm’s R&D intensity increases with 0,03. The explanatory power of model 2, or the goodness of fit, is slightly bigger than the explanatory power of model 1. The variance in R&D intensity is for 50% (R 2= 50%) explained by model 2. That is slightly more variance explained than model 1 does (R 2= 48%). The
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9
Independent variables Percentage Females - ,003*** - ,004*** - ,010*** - -,00004 -- (,001) - (,001) - (,003) - (,001) -Dummy Females - - ,132*** - ,152*** - ,299*** - ,028 - - (,029) - (,036) - (,060) - (,031) Control variables Firm size ,104*** ,101*** ,099*** ,128*** ,125*** ,128*** ,126*** ,078*** ,078*** (,008) (,008) (,008) (,010) (,009) (,020) (,019) (,008) (,008) Liquidity ratio -,016 -,007 -,009 ,035 ,028 ,043 ,045 -,046** -,044** (,019) (,019) (,019) (,023) (,023) (,039) (,037) (,020) (,020) Firm performance 2011 -,005*** -,005*** -,005*** -,006*** -,006*** -,005 -,002 ,004** -,004** (,002) (,002) (,002) (,002) (,002) (,003) (,003) (,002) (,002) Constant -1,116*** -1,147*** -1,194*** -1,459*** -1,501*** -1,601*** -1,706*** -,814*** -,839*** (,087) (,086) (,086) (,104) (,103) (,197) (,184) (,089) (,091) R-Square ,48 ,50 ,51 ,55 ,57 ,62 ,66 ,45 ,45 Adjusted R-Square ,46 ,47 ,49 ,54 ,56 ,56 ,60 ,41 ,41 F-test 21.29*** 21,02*** 22,48*** 58,87*** 63,49*** 9,50*** 11,33*** 11,53*** 11,64*** Number of firms 312 312 312 195 195 95 95 215 215
Table 10. Regression estimates of the regression analysis between board gender diversity and R&D intensity R&D intensity
36 adjusted R2 increases only if the new variable added improves the model. Therefore, it takes into account the number of variables added, and is always smaller than R 2.
A robustness check on the results is also performed. Instead of the proportion of females as the independent variable, a dummy variable is used which is coded 1 if the board contains females, and 0 if the board consists only of men. The results of this check are shown in the column of model 3 in table 10. This model is also significant, as is the positive relation between the dummy variable and R&D intensity (β = ,132 p < ,001). This means that companies with at least one woman in the board have a R&D intensity that is 0,132 higher, compared to companies with no women on their boards. That is a half standard deviation more, which is a relatively large impact. Table 11 gives an overview of the used variables in the different models employed.
Table 11. Specification of the used models Model
number Independent variable Industry Country 1 None All Industries All Countries 2 Proportion of Females All Industries All Countries 3 Dummy Females All Industries All Countries 4 Proportion of Females Manufacturing All Countries 5 Dummy Females Manufacturing All Countries
6 Proportion of Females All Industries Great-Britain and Ireland 7 Dummy Females All Industries Great-Britain and Ireland
8 Proportion of Females All Industries All except Great-Britain and Ireland 9 Dummy Females All Industries All except Great-Britain and Ireland
37 To conclude this paragraph, it can be said that the hypothesis is supported. Board gender diversity has a significant positive effect on R&D.
4.2 In-depth analysis
To check if the results remain robust in subsamples, additional tests are performed. First, the sample is analyzed using only companies who are active in the manufacturing industry. This is the industry where most companies of the sample are active in, and where the companies spend on average the most on R&D, compared to the other industries. So, we would expect the results remain robust, and perhaps be even stronger than the results of model 2. The results of this test are presented as model 4 and 5 in table 10. In model 4 the independent variable is proportion of females, in model 5 the independent variable is the dummy for females. Both these models are significant (model 4; F(4, 191) = 58,87, p < .001, model 5; F(4, 191) = 63,49, p < .001) and the regression coefficients are slightly bigger than the original model 2 (model 4; β = ,004 p < ,001, model 5; β = ,152 p < ,001). This means that there is a stronger effect of board gender diversity on R&D in the manufacturing industry, as compared to the whole sample. Logically, there should also be an industry where the effect is smaller than the effect of the whole sample. However, additional tests should be performed to found out which industry that is. Furthermore, of some industries the sample contains few companies. This makes regression analysis difficult.
38 Continental-Europe corporate governance system. All four models are significant. However, the regression estimates of the independent variable of model 8 and 9 are not significant, and in the case of model 8, points in a different direction than the previous found results (model 8; β = -,00004 p = ,965, model 9; β = ,028 p = ,368). The regression estimates of the Anglo-Saxon models are significant (model 6; β = ,011 p < ,001, model 7; β = ,310 p < ,001). These estimates are stronger than the results of model 2, which takes all countries into account. This means that adding a woman to the board of a company in Great-Britain or Ireland results in a larger increase in R&D intensity as opposed to the boards of the companies in the other countries of the model. The results of the Continental-Europe countries mean that there is no significant effect of adding a woman to the board on the R&D expenditures. This finding is remarkable. I will elaborate on this result in the next chapter.
39
5. Discussion and Conclusion
5.1 Discussion
The purpose of this paper was to find out if two goals of the European Union, a women quota for boards of directors and stimulating R&D activities, are contingent with each other. Therefore, a sample of 322 listed European companies is analyzed to examine the relationship between board gender diversity and R&D expenditures. This research is based on the paper of Miller and Triana (2009), who analyzed a sample of Fortune 500 companies to examine the effect of board gender diversity on innovation and find a significant positive relation. This paper’s analysis confirmed Miller and Triana’s (2009) finding and my hypothesis; the more gender diverse a board is, the more the company spends on R&D. Based on the general analysis performed, the answer to the research question is that the two goals of the European Union are contingent with each other. This positive effect can be explained from the fact that a diverse board is more effective in problem solving and decision making, because the variety of perspectives that emerges from a diverse board causes decision makers to evaluate more alternatives and to explore the consequences of these alternatives (Carter et al., 2003). Next to this, a diverse board can better understand the market which is becoming more diverse, and therefore anticipate better on the needs of that diverse marketplace (Smith et al., 2005). Furthermore, diversity increases creativity and innovation. Following Robinson and Dechant (1997), “attitudes, cognitive functioning and beliefs are not randomly distributed in the population, but tend to vary systematically with demographic variables such as age, race and gender”. Therefore, a gender diverse board is more willing to invest in innovation and spends more on R&D.
40 even reinforced it. On the other hand, the results of the Continental Europe sample contradicted the previous results. Based on this sample, no relation is found between board gender diversity and R&D expenditures. These are very notable results. There seems to be a difference in the relation between board gender diversity and R&D expenditures under the two corporate governance systems. The significant positive relationship of the Anglo-Saxon model is in line with Miller and Triana (2009), who investigate companies in the US, which also has an Anglo-Saxon corporate governance system. The outcomes of the in-depth analysis are in line with Van Veen and Elbertsen (2008). They investigated the effect of a corporate governance system on board nationality diversity and found significant results. They find that in boards in the UK there is much more nationality diversity in boards than in Germany. They argue that a great deal of this variance can be attributed to corporate governance regime features of the countries. The degree of board nationality diversity depends on the position of different
stakeholders (shareholders, employees and other board members) and the variety in institutionalized recruitment procedures which differ among corporate governance regimes (Van Veen and Elbertsen, 2008). This significant effect was found while controlling for three important company characteristics. A limitation of this paper is that there is no distinction made between supervisory and director members of boards, while the paper of Van Veen and Elbertsen (2008) does. Even between supervisory and director boards they find significant differences in nationality diversity. This means that the degree of nationality depends on a combination of country- and company-level forces.
41 Therefore the effect of an Anglo-Saxon board on R&D expenditures differs from the effect of a
Continental-Europe board on R&D expenditures.
Because most empirical research on board diversity has been restricted to US data, the generalizability of these findings cannot be extended across national boundaries due to different regulatory and economic environments, cultural differences, the size of capital markets and the effectiveness of governance mechanisms (Kang et al., 2007). In sum, there seems to be differences in board composition and their effect on corporate outcomes under different corporate governance systems. Consequently, as a direction for future research, the influence of various governance structures should be separately examined in each country. On top of that, these corporate governance regulations are changing rapidly.
Furthermore, this finding is also a direction for the European Union. With 28 different countries, imposing the same rules on all may not have the desired effect in all countries. Therefore, solutions need to be more tailor-made to reach the goal of more gender diversity and stimulating R&D activities across all countries within the European Union. Furthermore, one can even ask if imposing a women quota is the right way to achieve board gender diversity, but this question is not in the scope of this paper.
5.2 Conclusion
42 board diversity cannot be simply generalized to other countries, because of the different effects of the corporate governance systems on corporate outcomes. The managerial implication of this paper is for the EU who aims to impose a women quota on corporate boards of listed firms. The European Commission needs to stimulate board gender diversity in Great-Britain and Ireland, because in these countries a more gender diverse board has a significant positive effect on R&D expenditures. However, for other countries of the European Union, the European Commission needs to find other ways to stimulate R&D spending, because board gender diversity has no effect on R&D expenditures in these countries. In this search of other ways, the influence of the different corporate governance systems of the member states needs to be kept in mind.
5.3 Limitations and suggestions for further research
A couple of limitations of the research need to be mentioned. Some of these limitations serve also as input for further research. These limitations will be mentioned in the following paragraph. In the next paragraph, another suggestion for further research will be given.
43 solvability should be a better control variable. Fourth, high R&D expenditures do not guarantee this resource is also made effectively use of. Peterson and Jeong (2010) find that the processes of research and development were more important in determining firm-level financial performance than the amount of money spent on R&D. Therefore, the process of R&D needs to be efficient and closely monitored, to make sure resources are not wasted. So, R&D is an important input of the innovation process and thus can lead, if successfully, to greater firm performance, but it does not guarantee greater firm performance. This needs to be taken in mind when interpreting the results of this study.
44
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Appendix - List of used abbreviations
NACE Rev 2 - Nomenclature statistique des Activités économiques dans la Communauté Européenne revision 2, Statistical naming of economic activities in the European Community revision 2. It is an economical classification of the activities of companies which is set by the European Union. A company can be active in different industries. The NACE classification is based on the activity that brings the most value to the company. NACE was first introduced in 1970. Revision 2 is operative since 2008.
