BACHELOR THESIS
The influence of board diversity on the financial performance of Dutch educational
institutions
Ilse Boumeester, 10747419 January 31, 2018 Academic year 2017-2018 Bachelor Thesis of the BSc Economics and Business: Finance & Organization Specialization: Organization Supervisor: dr. S. Dominguez Martinez
Abstract
Board diversity is an important corporate governance issue. Today, there are still les women present in Dutch boards than men. This research investigates if gender and age diversity influences the financial performance of Dutch secondary educational institutions. The financial performance is measured with three indicators namely liquidity, solvency and the quality. Data from 50 secondary educational institutions is collected. The results show no significant results. Therefore, it can be concluded that there is no significant influence from board diversity on the financial performance of Dutch secondary educational institutions.
Statement of Originality
This thesis is written by Ilse Boumeester, who declares to take full responsibility for the content of this document. I declare that the text and work presented in this document is original and that no sources other than mentioned in the text and its references have been used creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Table of contents 1. Introduction 5 2. Context 7 3. Literature Review 8 3.1 Corporate governance 8 3.2 Gender diversity 9 3.3 Age diversity 10 3.4 Ethnical diversity 10 4. Methodology 11 4.1 Data 11 4.2 Variables 11 4.2.1 Dependent variables 11 4.2.2 Independent variables 12 4.2.3 Control variables 12 4.3 Regression model 13 5. Results 14 5.1 Descriptive statistics 14
5.2 First regression analysis 15
5.3 Second regression analysis 16
5.4 Robustness check 17
6. Discussion 18
7. Conclusion 19
References 20
Introduction
A lot of educational institutions faced financial problems in the past decade. For example, Amarantis, a Dutch non-profit organization which consists of more than sixty schools, was 92 million euros in debt in the fiscal year of 2012. The institution lacked clear leadership, there was financial
mismanagement, there was evidence of self-enrichment and conflict of interest (Dool, 2012). A similar financial problem arose in Rotterdam. The foundation BOOR, consisting of more than 140 schools had liquidity problems and asked for help from the municipality of Rotterdam. BOOR received 3.2 million euros to solve the problems and save the organization from going bankrupt (Rengers & Vasterman, 2015).
This phenomenon is also seen in the US and UK. Perry Beeches Academy Trust, an English school in Birmingham, had a net deficit of €2.8 million in 2016 (George, 2017). Leadership and management were found inadequate according to the Ofsted inspection report (Dickens, 2016). In California there were a record number of school districts which faced bankruptcy. The Inglewood Unified School District and 11 other districts could not pay the bills in 2012 or the next year (Watanabe, 2012). Consequently, more attention is paid towards the quality and financial performances of educational institutions worldwide.
A potential explanation for bankruptcy can be that corporate governance problems exist. The performance of an organization is influenced by corporate governance. According to Hart (1995) individuals work in an organization and follow instructions, but do not care per se about the
performance of the organization. This can lead to conflicts of interest with the management and vice versa. The Supervisory board monitors and controls the management and advices on major decisions. There are several reasons that could lead to insufficient performance of the board. First, most of the board members have other jobs or sit on other boards as well. Secondly, there is often little significant financial interest for the board members in the company.
The composition of the board and financial stability of the organization is becoming more important these days. It is the task of the board of directors to integrate both the organizational goals as well as the interest of the public stakeholders in the decisions that are made. Multiple studies found that adequate corporate governance benefits the organization by helping in making crucial decisions (Deloitte, 2017), and establishing a better financial position according to Rodriguez-Fernandes (2015).
Since 2015 there is a renewed supervision for the financial performance of Dutch educational institutions. Using this system, the board has a better view on the quality of education and the
financial performance. The board will take actions if needed, to improve the quality education and financial performance. If the educational institution is, or is likely to be, affected by financial problems, the education inspectorate can put the institution under extra financial supervision. The institution has to make a plan for recovery to improve the financial situation in the future. The supervisory board has an important role in decision making and advices the board. There has to be sufficient financial expertise in the supervisory board, therefore at least one of the members is advised to be financial qualified (Ministerie OCW, 2017).
Findings above suggest that the board is essential for the financial position of the organization and could also play an important role in the financial performance of the educational organizations. However, research into the quality of the board and financial performance is mostly based on publicly listed firms pursuing profits. It is unknown whether or not there is a relationship between board diversity and financial performance in non-profit firms, such as educational institutions. That is why this thesis will examine whether a relation exists between board diversity and the financial
performance of nonprofit firms, in particular Dutch secondary educational institutions.
studies might not be applicable to educational institutions. Findings from these studies are however contradictory. Shehata, Salhin & El-Helaly (2017) found a significant negative effect of age and gender diversity on firm performance. In addition, the relation between the age of the board and firm performance, regardless of gender composition, was negative in their research. Campbell and Mínguez-Vera (2017) found on the other hand a positive relation between gender diversity and firm performance.
Today, in the Netherlands the board of directors still consists of less women than men
(Lückerath, 2017). However, women could possibly benefit from the fact that there is less competition in non-profit firms than in listed firms, as women have the tendency to underperform in competitive situations compared to men. I expect there to be more women in the educational sector. The gender and age diversity may play a role. Therefore, the main research question is:
Does diversity in the Supervisory Board influence the financial performance of Dutch secondary School foundations?
The structure of this thesis is as follows; the next two chapters will give an overview of the existing literature on board diversity and the financial performance of Dutch school foundations and listed firms. Thereafter in chapter 4 the methodology will be described and in section 5 the results will be presented. Finally, in section 6 the discussion will be presented.
2. Context
2.1 Corporate governance for education in the Netherlands
Dutch secondary education has to follow the code ‘Goed Bestuur VO’. This contains six
requirements for the ‘VO-raad’. These requirements relate to the accountability, participation and the preventions of conflicts of interest. In addition, there are 47 guidelines that should be followed by the explain or apply method. Annual reports have to be made public, with rules regarding mentioning the statutes, the board’s rules and regulations on functioning of the board and internal monitoring. It is of great importance to improve and maintain a good educational system. Secondary educational
institutions are funded by government and serve public interests. Therefore, it is important to make these reports visible (VO-raad, 2015).
The Dutch government states that the quality of education is sufficient, but must improve in the future to maintain a good educational system. The government is taking steps to improve the quality. For example, it wants to improve teaching in a second language and educate more maths to pupils in secondary schools. Every board in secondary education receives one budget for staff and material. This budget is called the lump sum. Secondary schools receive an additional budget via the ‘prestatiebox’. This is a fixed amount of money schools receive per student for extra development of talent or other developments (Rijksoverheid, 2017). The Ministry of education, culture and Science (2017) distributes the budgets every year on the first day of January. This budget is based on the number of pupils the school counts on first day of October of the previous year. Sometimes the supervisory board has multiple schools to represent. In such a case, the supervisory board decides how to divide the money among the schools.
2.2 Board composition Dutch education
The most common board structure in the Netherlands is the two-tier structure. Within organizations there are two separate boards, namely the supervisory board and the board of directors. The board of directors plays an important role in the organization. It bears responsibility for decision making and the quality of the given education. The quality, continuity and safety of the students are important to maintain for the management of the school. The school management makes important decisions, but the board of directors is held responsible for every decision, so it must carefully and critically take a look at the decisions made by the management. The supervisory board gives permission for certain decisions and advises the board of directors. This research focuses on the supervisory board, because there is financial expertise and the board advices on the important decisions regarding the school and financial decisions.
There is little empirical evidence about the relationship between governance and education. Zaman (2015) investigated the relation between good governance and higher educational outcomes. The results suggest that good governance has a positive influence on education. There are only few empirical studies that actually say something about the relation between board performance and education. Secondly, it is hard to compare evidence about educational systems due to differences between countries. The composition of the boards and governance models are different in Europe in comparison to other countries outside Europe. (NRO, 2017).
3. Literature review
In the next chapter the theory regarding board diversity and firm performance will be explained. Prior research on the relation between board diversity and firm performance will be discussed. First, the idea of corporate governance will be introduced with multiple theories that support board diversity and theories that do not support board diversity. Secondly, the corporate governance in the Dutch educational system will be considered. Thereafter, board diversity in relation to performance will be discussed. At last, the hypotheses will be derived.
3.1 Corporate governance
Corporate governance is a term to indicate how businesses are managed and how managers can be monitored to prevent fraud and self-serving behavior. It deals with how managers maximize the firm’s value and make business decisions. On the other hand, it deals with how suppliers want to ensure return since they participate with their money. The manager and the investor sign a contract to determine what will happen with the invested money and how the return is determined (Schleifer & Vishny, 1997).
Corporate governance is important regarding the agency theory, because it deals with separation of ownership and control. The agency theory explains the relation between principals and agents in a business transaction. The role of the board is to monitor, control and resolve issues between the stakeholders and the management. This is a fundamental concept of the agency theory
(Jensen & Meckling, 1976). Often there are conflicts of interest between the board, managers and
workers. The agent is self-centered according to this theory, therefore the agent can take the interests of the principal less into consideration. According to Carter et al., (2003) a more diversified board may control and monitor managers in a better way, because the independency of the board is
increased. Agency theory supports a link between financial performance and board diversity, but there is no clear evidence.
According to the resource dependency theory,the use of external resources by organizations influences the organization’s behavior. The environment provides rare resources and firms depend on those critical resources in order to continue and grow. These resources are managed by setting up different forms of organizational arrangements (Drees & Heugens., 2013). Pfeffer and Salancik (1978) argue that every single board member brings their own valuable resources to the firm. These members will bring information, skills and a broad network that might positively influences the organization. Within this theory gender, age and ethnicity provide different perspectives to the board and therefore add to firm resources.
The human capital theory states that employees are considered as the economic value of skills, knowledge and experience in the work field. These competences can be developed by the employees. Zald (1969) argues that board members provide unique qualities for the organization based on their background. This influences the performance of the board when there is more diverse and unique human capital.
Summarizing above theories, the agency theory, resource dependency and the human capital theory do not provide clear evidence for the relation between board diversity and firm performance based on prior research, but suggest a positive relation (Carter et al., 2010)
There also exist theories that do not support a positive relation between board diversity and performance. Regarding the social identity theory, people have strong preferences to identify themselves within numerous categories. For example, religious affiliation, organizational membership, gender and age. The identification of the categories serves two functions. The first function is that people can allocate someone in a category with specific characteristics for that particular person. The second function is that people are enabled to define themselves in a social
category in an environment. A mixed gender group may come across conflicts which do not appear in a group that is homogeneous. This enhances the efficiency of decision-making (Tajfel & Turner, 1985).
The similarity-attraction theory argues that people are attracted to factors such as values, beliefs and attitudes. According to this theory people with the same preferences for certain social factors tend to like each other, and are therefore more likely to cooperate without conflict (Byrne, 1971). This theory does not support diversity, because homogenous boars are more likely to cooperate without conflicts.
The social categorization theory is another theory that gives a broader explanation of board performance. Individuals categorize themselves in a social group with characteristics based on ethnicity, gender and race. They find people with the same social characteristics in group terms (Bober & Bornewasser, 1987). Dobbin and Jung (2011) suggest that people rather work with people similar to themselves. This results in lower efficiency when the board consists of a more diverse range of people.
3.2 Gender diversity
There are a few researches that investigated gender differences amongst members of boards of educational institutions. But these researches do not investigate the influence of diversity on the financial performance. According to Bernard (1981), the desired experience for female board members differs from the experience for male board members when applying for a job. Shakeshaft (1988) states that female board members, compared to men, spend more time with people, are able to communicate better, care more about one another, are closer with students and the teachers than men and the women motivate more active.
According to Shakeshaft (1988) women are much more involved in communication and emotional problems. Some researchers used surveys to gather information about gender diversity in school boards. One of the first was the research of Counts (1927). In his sample, he found that 90% of the board consisted of men. The same number was found thirty-five years later. In the early 1970’s, when there was increasing attention for gender diversity within schools. In 1972, the National School Boards Association spread a survey and found that 12% of board members were female. This was 33% in 1978. (McCloud, 1990).
Blanchard (1977) gave 1280 board members a questionnaire about decision-making. The results suggest that the presence of women in school boards leads to healthier decision-making and this supports diversity amongst board members. According to Blanchard (1977) there is no adequate deliberation without conflicts between board members. Researchers such as Morrissey (1971) and Saiter (1978) investigated the difference in behavior of female and male members of the school board. The first difference they found was that men with the same position in the board as females were less educated compared to a female board member. The second difference is that women worked on more tasks related to school than men. The final difference is that women prioritize the quality of education over financial matters. It is important to recognize the differences between gender in order to improve effectiveness of decision-making (Moore, 1998).
Recent research on gender diversity is mainly focused on listed firms. These researches will be discussed next. Erhardt, Werbel and Shrader (2003) indicate a positive relation between gender diversity in the board of directors for 127 large US companies and the financial performance. A research executed by Lückerath-Rovers (2011) investigates the relation between women in boards and the firm performance in the Netherlands. There were 97 Dutch listed firms included in the research. There is a minority in firms that has at least one women in the board of directors, namely 45.6%. The results increased compared with the research a year earlier, where the percentage of firms with at least
one woman in the board was 39% (Lückerath-Rovers, 2010). Carter et al. (2010) found a significant positive relationship for women on the board and the return on assets in US firms between 1998 and 2002. Campbell & Minguez-Vera (2008) used panel data analysis in Spain and found a positive relation between gender diversity and board performance.
Adams and Ferreira (2009) found that an increase in gender diversity among the board, results in a decreasing stock price and decreasing profit. A negative relationship between gender diversity and firm performance is suggested by Smith et al. (2006). Whereas Blackburn & lles (1997) did not find a significant relationship at all.
There is no evidence on gender diversity and the financial performance of nonprofit
organizations derived from the literature. The theories show mixed results. The resource dependency theory suggests a positive relationship, because each board member will bring own resources to the organization. Since there is no aim for profit in the organization, self-serving behavior and conflicts of interest could be less of a problem. Nowadays the percentage of female board members is increasing and this might positively influence the financial performance, since female board members will bring new resources to the board. Therefore, I expect that gender diversity positively influences the
financial performance of the Dutch educational institutions.
H1: Gender diversity positively influences the financial performance of Dutch educational institutions
3.3 Age diversity
Next, the literature on the relation between age diversity and financial performance will be discussed. The age of the board needs to be diversified in order to meet different perspectives of customers within a firm according to Huse and Rindova (2001). According to Houle (1990) an older board member is more likely to be experienced and often bring more resources to the organization compared to a younger board member that can bring new insights and energy to the organization or is willing to take more risk. McIntyre, Murphy and Mitchell (2007) investigated Canadian listed firms and found that age diversity is beneficial for performance of the firm. Bantel & Jackson (1989), found that age diversity is beneficial for making innovative decisions and this influences the performance of the firm in a positive way.
Richard and Schelor (2002) performed a quantitative meta-analysis for top management and found no clear evidence of a relation between age diversity and firm performance. Ali et al. (2014) found a negative relation between age diversity and the firm performance measured by the return on assets. People from the same age tend to interact more with each other within boards. Ali et al. (2014) found a positive relation if age diversity was regressed on the sales of growth.
Findings of the studies mentioned above are not consistent. Age diversity can bring different skills, information and a greater network and other energy to the board according to the human capital theory. The percentage of the supervisory board and the executive board in Dutch secondary
education with the age above 50 measured in 2016 is 71,1% (Rijksoverheid, 2016). Therefore, I expect there to be a positive influence from age diversity on firm performance in Dutch educational institutions. I expect the institutions with a more varied board in age to perform better, because there are more skills, information and a greater network available.
H2: Age diversity positively influences the financial performance of Dutch educational system 3.4 Ethnic diversity
Besides gender and age diversity, the ethnical differences among board members will be discussed. In the research performed by Ruigrok, Peck & Tacheva (2007) the results suggest that ethnic diversity is positively related to firm performance. Teams with more national diversity will bring more
knowledge from different perspectives to the firm, because of the different environments. For this research, the focus will only be on age and gender, because it is hard to measure the exact ethnicity of the board members.
4. Methodology
This chapter will discuss the methods that are used for this research. First, the hypothesis will be derived. Thereafter, the data will be explained which consists of the dependent variables, independent variables and the control variables. Lastly, the regression analyses will be presented.
4.1 Data
Financial information from secondary educational institutions is retrieved from the database XBRL. The annual reports of the institutions are only available from the year 2015 and 2016 in the database from the government site. All the data is gathered in an Excel spreadsheet. A cross-sectional study is performed, because there is limited data available. The solvency and the liquidity for the year 2015 and 2016 are in the database. The information about the board characteristics is not available in the database XBRL1, therefore the annual reports of the selected educational institutions are used as well. In the section ‘staff’ the average age of the indirect and direct personnel is available. The section ‘students’ contains the number of students and the number of schools. At last, the gender of the board and the number of board members can be found in the section ‘Supervisory Board’. Educational institutions are obligated to publish their annual reports online according to the code ‘Goed bestuur’2. There are different levels of education within the Dutch secondary education. Some schools are part of an institution that supervises several schools. Some of the institutions that were
investigated supervises only one school. The institutions that include all different levels of education are selected for a random sample. There were 96 institutions in the database. Not all the information necessary for this research could be collected from the annual reports in the database or from the internet. The total amount of institutions that is available for the use of this research is 50. The number of board members are the same for the two years in this sample. The Inspection of Education (2016) uses five financial factors which reflects the financial position of the educational institutions in their investigation. If the numbers are less than the established thresholds further inspection will follow, therefore two of the factors will be used as a performance measure.
4.2 Variables
In this section, the variables used for this research will be presented and it will be explained why they are of importance.
4.2.1 Dependent variables
The financial performance is measured in three ways, since it is recommended to use multiple measures. There are multiple factors that measure same dependent variable according to Chiang, Jhangiani & Price (2016). The first performance indicator is the liquidity. The liquidity is calculated by dividing the current assets by the current liabilities. In general, it is better to have greater coverage for the case where the liabilities in the short-term have to be paid. There are several studies that consider liquidity as an important financial measure (Kamnikar, Kamnikar, and Deal (2006); Zafra-Gómez, López-Hernández, and Hernández-Bastida (2009); Atan et al. (2010)).
The second financial performance indicator is solvency. It measures the extent to which assets will cover the liabilities on the long-term. This is calculated by dividing the total assets by the total
1 Financial database annual reports educational institutions 2 Code good governance for educational institutions
liabilities. Previous research used solvency as a financial performance measure
(PricewaterhouseCoopers (2006) & Ehreth, (1994)) These two financial indicators are chosen because the educational institutions are not profitable and there is no Tobin’s Q or the Return on Equity is not applicable.
The last financial indicator is the quality of educational institutions. Quality can be influenced by the decisions the supervisory board makes. According to Broadbent et al. (2003) there are several ways of measuring the quality of educational institutions. The quality of the Dutch secondary schools is measured by the Inspection of Education. Elsevier constructed a research with extra facts regarding children who did not pass the school year and exams, in addition to the judgements of the Inspection of education. This research is more extensive than the Inspection of Education, therefore the research of Elsevier will be used. Each school is rated based on four criteria. The first criterion is the position of students in third grade compared to the advice they received in elementary school. The second one is the educational level in the first three years of secondary schools. The third is the structure of education in the next years. The final criterion is the exam rating. There are four categories; not enough, sufficient, good and 'super school'. The ranking of the institutions based on the four categories is used as a measure for quality.
4.2.2 Independent variables
The first independent variable is for measuring the gender diversity among the Supervisory board. The Blau index for the number of women in the board will be used. The values of the Blau index vary from 0 to a maximum of 0.5 if the board is equally divided. Campbell and Minguez-Vera (2008) argue that the Blau index is at its maximum if the board is equally divided, and therefore the degree of homogeneity is minimalized. The formula for the Blau index is:
Where pi is the percentage of the board members for each of the two gender categories.
The second independent variable is age diversity. The average age of the indirect personnel is used per institution, because that is the only data available. The indirect personnel are the people that are not directly linked to the organization, such as the supervisory board. According to Ferrero-Ferrero et al. (2012) age diversity can be measured in several ways. The standard deviation of age is one way to measure age diversity, therefore this research will use the standard deviation of the indirect personnel. The age diversity is measured by the standard deviation of the age of the indirect personnel, because the spread will be used.
4.2.3 Control variables
The size of the institution will be used as a control variable. This measures the number of schools each institution represents. French and Farma (1992) found that the size of firms is related to the performance of the firms. In this research SCHOOL will represent the size. The number of students vary a lot per school. Therefore, it is used as a control variable and is defined as STUDENTS. The number of students are divided by 1000, because of the large numbers.
The last to discuss is the size of the Supervisory board. According to Carter et al. (2003) larger boards will have more women in the board. This is defined as BOARDSIZE.
4.4 Regression model
In this section the result will be presented. The regressions are executed three times using the Ordinary Least Square Method (OLS) to test if board diversity will influence the financial
performance of Dutch educational institutions. First, a regression with gender diversity is executed. The first regression uses solvency as a performance indicator. The second regression uses liquidity as a performance indicator. The final regression uses the quality of the institutions as a performance indicator. The second regression is repeated with age diversity as an independent variable instead of gender diversity. The results are only presented from the year 2016, because the results do not differ significantly from each other.
The first analysis uses BLAUGENDER as the independent variable. Regression 1:
LIQ = β0 + β 1 BLAUGENDER + β 2 SCHOOL + β 3 STUDENTS + β 4BOARDSIZE + εi Regression 2:
SOLV = β0 + β 1 BLAUGENDER + β 2 SCHOOL + β 3 STUDENTS + β 4BOARDSIZE + εi Regression 3:
QUAL = β0 + β 1 BLAUGENDER + β 2 SCHOOL + β 3 STUDENTS + β 4BOARDSIZE + εi The second analysis uses AGE as the independent variable.
Regression 4:
LIQ = β0 + β 1 AGE + β 2 SCHOOL + β 3 STUDENTS + β 4BOARDSIZE + εi Regression 5:
SOLV = β0 + β 1 AGE + β 2 SCHOOL + β 3 STUDENTS + β 4BOARDSIZE + εi Regression 6:
QUAL = β0 + β 1 AGE + β 2 SCHOOL + β 3 STUDENTS + β 4BOARDSIZE + εi Where:
LIQ = liquidity as a performance indicator SOLV = solvency as a performance indicator QUAL
BLAUGENDER AGE
= quality as a performance indicator = blau index for gender heterogeneity = standard deviation of age
b1 > 03 b1 >04 SCHOOL = number of schools in institutions b2 >0 STUDENTS = number of students in institutions b3 >0 BOARDSIZE = size of the Supervisory Board b4 >0 εi = residuals from institutions 1 till 55
The regression is performed two times for the different measures of board diversity. If the age and gender diversity increases, the financial performance will increase following the hypotheses. The OLS regression uses robust standard errors to obtain unbiased standard errors and a correlation table is presented to check for multicollinearity. A robustness check is done for gender diversity to see if the results vary with a different method of measuring gender diversity. The results from the year 2015 are attached in the appendix.
3 coefficient from the first regression performed with BLAUGENDER 4 coefficient from the second regression performed with AGE
5. Results
In this section the results from the OLS regressions will be presented. The additional results from the year 2015 and the robustness check are attached in the Appendix. It begins with the descriptive statistics of the 50 educational institutions and the correlation table. Thereafter, the analyses of the hypotheses will be presented. The significant coefficients are presented with * behind the numbers. 5.1 Descriptive statistics
Table 1 gives an overview of the data from the data gathered from the institutions. Table 2 shows the correlation between the included variables for the regressions. Figure 1 shows the distribution of the gender diversity based on the Blau index.
Table 1 – Summary statistics
Variable N Minimum Maximum Mean Std. Dev
LIQ 50 0.55 4.94 2.06 0.97 SOLV 50 -0.11 0.73 0.48 0.16 QUAL 50 1 4 2.26 0.63 BLAUGENDER 50 0.00 0.50 0.42 0.11 AGE 50 1.87 3.86 2.98 0.53 SCHOOL 50 1 11 3.80 2.02 STUDENTS (x1000) 50 0.80 21.33 3.23 2.85 BOARDSIZE 50 4 13 5.96 1.50
Table 2 – Correlations between all variables
BLAUGE~R AGE
SCHOOL STU~1000 BOARDS~E
BLAUGENDER 1.000
AGE
0.012
1.000
SCHOOL
0.001
0.181 1.000
STUDENT~1000 -0.284
0.117 0.336
1.000
BOARDSIZE
0.122
-0.086 0.254
0.039
1.000
Table 2 There is a negative correlation of -0.284 between the number of students and gender diversity. Age diversity is negatively correlated with the board size. The other control variables are positively correlated with each other. There are no extreme high correlations, and therefore multicollinearity will not be a problem in the regressions performed.
Figure 1 – Distribution of the gender diversity
Figure 1 shows that the frequency of an equally distributed board regarding gender is relatively high. The mean is 0.417 and this means that several boards are equally distributed or close to an equally distributed board.
5.2 First analysis
Table 3 shows the results of the first analysis performed and will give an answer to the first
hypothesis; ‘Gender diversity positively influences the financial performance of the Dutch secondary educational institutions’. For every financial performance measure, there is a regression with the Blau index with no other variables and a second regression with all the control variables included. In total, there are six regressions performed.
Table 3 – First analysis results
Note: Significant at *10% **5% ***1% (s.e) are Robust standard errors
The R2 gives information about how much of the variance is explained by the model. In the regression with LIQ only 4.35%-13.02% of the variance is explained by the two models, in the
LIQ SOLV QUAL
(1) (2) (1) (2) (1) (2) BLAUGENDER 1.770 1.564 -0.013 0.004 -0.240 -0.399 (1.332) (1.394) (0.193) (0.196) (0.677) (0.735) SCHOOL -0.039 0.022* 0.030 (0.069) (0.013) (0.045) STUDENTS -0.056* -0.001 -0.012 (0.028) (0.004) (0.021) BOARDSIZE -0.120* -0.017 0.044 (0.071) (0.014) (0.047) _cons 1.327** 2.456*** 0.484*** 0.496*** 2.360*** 2.085*** (0.560) (0.710) (0.077) (0.116) (0.274) (0.369) R2 0.0435 0.1302 0.000 0.074 0.002 0.026 Adj. R2 0.0236 0.0529 -0.021 -0.009 -0.019 -0.061 N 50 50 50 50 50 50
regression with SOLV this equals 0%-7.4% and in the regression with QUAL it equals 0.002%-2.6%. The variables STUDENTS and BOARDSIZE are significant at 10% in the second regression with LIQ as the dependent variable. In the second regression with SOLV as the dependent variable SCHOOL is significant at 10%. It can be concluded that there is no significant influence from gender diversity in the Supervisory board on the financial performance of the Dutch secondary educational institutions.
5.3 Second analysis
Table 4 will give an overview of the results from the regression analysis with AGE as an independent variable. There are six regressions performed with three financial performance measures. The second hypothesis will be tested.
Tabel 4 – Second analysis results
Note: Significant at *10% **5% ***1% (s.e) are Robust standard errors
The R2 is for all regressions higher when the control variables are added, but the differences are not big. In the regression with LIQ only 0.1%-10.4%% of the variance is explained by the two models, in the regression with SOLV this equals 1.8%-7.9% and in the regression with QUAL it equals 1.1%-3.2. The variable STUDENTS is significant at 1% for the second regression with LIQ as a
performance measure. From the second regression with AGE it can be concluded that there is no significant effect from age diversity on the financial performance of Dutch secondary educational institutions.
5.4 Robustness check
To check if there is a significant difference if gender diversity is measured in a different way, the variable WOMEN is added. The variable measures the number of women present in the supervisory boards of the Dutch educational institutions. Table 5 presents the descriptive statistics. Table 6 shows the results for gender diversity with the variable WOMEN. There are six regression analyses
performed.
LIQ SOLV QUAL
(1) (2) (1) (2) (1) (2) AGE 0.067 0.117 -0.041 0.024 0.128 0.130 (0.260) (0.275) (0.042) (0.043) (0.182) (0.178) SCHOOL -0.038 0.021 0.022 (0.068) (0.013) (0.044) STUDENTS x (1000) -0.077*** -0.002 -0.008 (0.028) (0.004) (0.020) BOARDSIZE -0.101 -0.015 0.047 (0.077) (0.015) (0.045) _cons 1.866** 2.707** 0.356*** 0.425*** 1.877*** 1.534*** (0.846) (1.082) (0.131) (0.146) (0.516) (0.549) R2 0.001 0.104 0.018 0.0792 0.011 0.032 Adj. R2 -0.020 0.024 -0.003 -0.003 -0.009 -0.054 N 50 50 50 50 50 50
Table 5 – Descriptive statistics
N Minimum Maximum Mean Std. Dev
LIQ 50 0.55 4.94 2.06 0.96 SOLV 50 -0.11 0.73 0.48 0.16 QUAL 50 1 4 2.26 0.63 WOMEN 50 0 4 2.20 1.03 AGE 50 1.87 3.86 2.98 0.53 SCHOOL 50 1 11 3.80 2.02 STUDENTS (x1000) 50 0.80 21.33 3.23 2.85 BOARDSIZE 50 4 13 5.96 1.49
Table 6 – Third regression
Note: Significant at *10% **5% ***1% (s.e) are Robust standard errors
The regression with WOMEN as a dependent variable shows no significantly differences with the regression with BLAUGENDER performed. The only differences are that the variables BOARDSIZE and STUDENTS are significant at 5% in the second regression with LIQ as dependent variable. It can be concluded that there is no significant influence from gender diversity in the Supervisory board on the financial performance of the Dutch secondary educational institutions.
LIQ SOLV QUAL
(1) (2) (1) (2) (1) (2) WOMEN 0.098 0.180 -0.023 -0.019 -0.05 -0.124 (1.849) (0.161) (0.031) (0.032) (0.081) (0.087) SCHOOL -0.028 0.022* 0.027 (0.068) (0.012) (0.045) STUDENTS x (1000) -0.059** -0.003 -0.019 (0.026) (0.004) (0.020) BOARDSIZE -0.170** -0.010 0.085 (0.071) (0.011) (0.050) _cons 1.848*** 2.981*** 0.530*** 0.506*** 2.37*** 1.988*** (0.323) (0.077) (0.060) (0.077) (0.188) (0.280) R2 0.011 0.125 0.022 0.083 0.007 0.050 Adj. R2 -0.010 0.0469 0.001 0.002 -0.041 -0.036 N 50 50 50 50 50 50
6. Discussion and limitations
In this section the findings of this thesis will be discussed. The goal of this thesis was to examine the influence of board diversity on the financial performance of Dutch secondary educational institutions. The results will be compared to the existing literature regarding board diversity on the financial performance of firms. Lastly, the discussion will contain limitations for further research.
The hypothesis for this research is that gender diversity positively influences the financial performance of Dutch secondary educational institutions. Gender diversity in this research is measured with the Blau Index. The analysis shows no significant relation exists between board diversity and the financial performance of Dutch secondary educational institutions. There is no significant difference with the robustness check. The quality seems less relevant as a performance measure than liquidity and solvency for the first regression and the robustness check regression. The first explanation why there is a difference in results is that there might not be differences between men and women. There are other external factors, such as expertise and skills that may influence the performance of the board which are not relatable to the gender of the board members. The second explanation for the difference in the results is that the setting of this analysis is different than previous research. This analysis is based on nonprofit institutions, while the literature is based on publicly listed firms. The goal of the organization is different and therefore the results can differ. The third explanation is that there already might be more gender diversity in the educational sector. The gender diversity measured with the Blau index has a relatively high mean, which indicates that there are boards with more women in this sample.
McIntyre, Murphy and Mitchell (2007) investigated the effect from age diversity in Canadian companies on firm performance and found a positive relationship. The human capital theory is used to support the positive influence from board diversity on the financial performance. Following the hypothesis from this research, age diversity was expected to have a positive influence on the financial performance of Dutch educational institutions. However, for age diversity, the results do not suggest any significant influence from board diversity on the financial performance of Dutch educational institutions. An explanation is that members are mostly elder people with often more work experience. This could be a reason why the result is different than expected.
There are multiple limitations that could possibly explain the insignificant effect from board diversity on the financial performance. The research for this thesis contains only 50 secondary educational institutions, this decreases the power of the test. The database from the government contains more than 50 institutions so for further research this should be considered. In this setting educational institutions are investigated, but every educational institution contains one or several schools. The financial position and quality per school varies, while this research took into account the overall performance of the grouped schools. The second limitation is the range of the data, since there are only two specific years used for this research. If there is more data available this is recommended to use. The third limitation is the measurement of the quality of education. It is not a completely reliable measure and the result could be affected by this. Quality is a broad concept and there are many ways it could be measured. It is a subjective measure. In addition, the quality for education is not the same in every country. Further research is necessary to determine a proper measure for quality of education. The fourth limitation is omitted variable bias. As already mentioned above, there are variables that cannot be measured correctly but still influence the financial performance of the Dutch educational institutions. For example, expertise of the board members is not included. This could influence the financial performance.
6.2 Conclusion
Dutch secondary educational institutions. Data is collected from the government database and the annual reports of the institutions. A cross-sectional study is performed with three different performance measures used to test the hypotheses about gender diversity and age diversity.
Contrary to the hypotheses, gender and age diversity have no significant influence on the financial performance for this research. Both effects were relatively small and therefore further research is necessary to draw a conclusion.
Finally, the implications for the Dutch educational system cannot be given due to the insignificant results. Nowadays there is still a large difference in gender diversity, but the growing number of women in top titles would suggest further research in the future.
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Appendix 1: Regression output for 2015 Regression 1 Gender diversity:
Note: Significant at *10% **5% ***1% (s.e) are Robust standard errors
Regression 2 for age diversity:
Note: Significant at *10% **5% ***1% (s.e) are Robust standard errors
LIQ SOLV QUAL
(1) (2) (1) (2) (1) (2) BLAUGENDER 2.147 2.101 0.071 0.097 -0.24 -0.423 (1.502) (1.560) (0.173) (0.180) (0.677) (0.724) SCHOOL -0.122* 0.018 0.028 (0.070) (0.013) (0.044) STUDENTS (x1000) -0.037 0.003 -0.015 (0.030) (0.005) (0.019) BOARDSIZE -0.110 -0.008 0.046 (0.071) (0.013) (0.047) _cons 1.175* 2.433*** 0.442*** 0.403*** 2.36*** 2.104*** (0.633) (0.779) (0.070) (0.107) (0.274) (0.363) R2 0.0552 0.185 0.003 0.063 0.002 0.026 Adj. R2 0.036 0.113 0.018 -0.021 -0.019 -0.061 N 50 50 50 50 50 50
LIQ SOLV QUAL
(1) (2) (1) (2) (1) (2) AGE -0.011 0.128 0.039 0.024 0.111 0.099 (0.308) (0.320) (0.047) (0.048) (0.192) (0.191) SCHOOL -0.112* 0.017 0.022 (0.070) (0.013) (0.043) STUDENTS (x1000) -0.064** 0.001 -0.011 (0.031) (0.004) (0.018) BOARDSIZE -0.089 -0.007 0.044 (0.070) (0.014) (0.047) _cons 2.104** 2.854** 0.349** 0.367*** 1.911*** 1.641*** (1.013) (1.122) (0.157) (0.158) (0.576) (0.569) R2 0.000 0.141 0.015 0.064 0.008 0.026 Adj. R2 -0.028 0.064 -0.005 -0.019 -0.013 -0.064 N 50 50 50 50 50 50
Appendix 2: The institutions Almere College, CvB
Stg. Minkema Ond. Woerd. e.o. Stichting Waerdenborch Stichting Winkler Prins Stichting OSG Singelland Stichting o.s.g. Piter Jelles Stichting O.R.S. Lek en Linge Stg v Chr VO in het Gooi Stg v Opb VO Wageningen Sticht. Montessori Scholengem. Ver. v. Chr. Voortgez. Onderw. Stichting VO Regio Meierijstad Stg. Kath. VO Dongemondgebied STICHTING MERIDIAAN COLLEGE Stichting Het Assink Lyceum
Ver. Geref. Vgz. Onderw. M-Nl. Stg. voor bijz. VO Ubbo Emmius Stg. Vgz. Montessori Onderwijs St. Chr. Onderwijs Groningen Samenw.stg. Voortg. Ond. Uden Stichting Vechtdal College Stg. Chr. V.O. 'De Driestar' Samenw.stg. Voortg. Ond. Venlo Stg. KSG voor VO Best-Oirschot Openb. St. Sch.gem. Reigersbos Stichting Scholengroep Pontes Stichting Roelof van Echten Stg Scholengroep Den Haag Zd-W Stg. CO Over- en Midden-Betuwe Stichting RSG Magister Alvinus Stg. O.V.O. Ooststellingwerf Sticht. S.w.s. A.B.V.O. IJburg Stg. Voortg. Onderw. Lelystad Stg. Willem de Zwijger College Stichting Calvijn College Stichting Cambium College ovo Stichting Atlas College
Stichting Esdal College Sticht. Anna van Rijn College Staring College
Stg. dr. Aletta Jacobs College Stg. Nuborgh College
Het Baken Almere, Stichting Stichting Coenecoop College Ver CVO N & M Drenthe Stichting Fioretti Teylingen Stg. Kath. V.O. Heerhugowaard Stichting Tabor College
Stichting Metameer
