‘The influence of board capital on the number of problems financial firms faced during the 2007-2009 financial crisis’

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‘The influence of board capital on the number of problems financial

firms faced during the 2007-2009 financial crisis’

Master Thesis International Business and Management University of Groningen, Faculty of Economics and Business

March 31, 2014

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Page 1.0 Abstract 3 2.0 Introduction 4 3.0 Theory Section 5 3.1 Board heterogeneity 6 3.2 Human capital 7 3.2.1 Gender 7 3.2.2 Education 8 3.2.3 MBA 8 3.3 Social capital 9 3.3.1 Work experience 9 3.3.2 International experience 10 4.0 Methodology 11 4.1 Sample 11 4.2 Time frame 11 4.3 Data collection 11 4.4 Variables 11 4.5 Method of analysis 14 5.0 Results 16

5.1 Description of the data 16

5.2 Correlation 17

5.3 Collinearity 19

5.4 2006: The influence of board capital on problems 19

5.4.1 Control variables 20

5.4.2 Human capital 20

5.4.3 Social capital 21

5.5 2009: The influence of problems on board capital 23

5.6 2011: The influence of problems on board capital 26

5.7 Board capital over time 28

6.0 Discussion and Conclusion 32

6.1 Discussion 33

6.2 Limitations and future research 34

6.3 Conclusion 35

7.0 References 37

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1.0 ABSTRACT

The aim of this research is to explore the relationship between board capital and the problems faced by the 50 largest financial TNCs during the global financial crisis in the period of 2007-2009. The results show that board capital in 2006 did not significantly influence the problems during the crisis, and those problems did not significantly affect board capital after the crisis. However, the number of MBAs on a board turned out to be significantly related to the number of problems faced and vice versa. This finding encourages the ongoing debate of the additional value of MBAs on a board.

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2.0 INTRODUCTION

The cause of the financial crisis is a widely discussed topic. Jagannathan et al. (2013) stated the common wisdom is that cheap money and negligent supervision of financial institutions led to the financial crisis. However, they argued the financial crisis was just the symptom, and that the cause of the crisis is the huge labor supply shock the world experienced, leading to an excess in liquidity and money supply. Nonetheless, the cause does not take away the consequences. The financial industry was hit hard by the crisis. In the period from 2003-2007, 10 US banks were reported to have collapsed. Since the beginning of the crisis in 2007, over 25 US banks went bankrupt, and several other banks were rescued by the government just in time. Due to the crisis, banks had to reconsider their strategies and make some radical changes.

Usually, the board of a firm is held responsible for firm performance. Therefore, it might be reasonable that the boards of those TNCs which got in trouble during the crisis would be disciplined during or after the crisis. Firms should ensure a board is capable of executing its job. The capacities of a board can be estimated by analyzing board capital. Board capital consists of both human capital (experience, expertise, reputation) and relational/social capital (network of ties to other firms and external contingencies) (Hillman and Dalziel, 2003). Hillman and Dalziel (2003) stated that the presence of board capital will result in the provision of resources to the firm by the board, which has in turn been linked to firm performance in previous research. Since the crisis caused problems for many banks, the question rises whether the current boards at the time the crisis started, possessed the board capital necessary for the firm to be run well. This leads to the main question in this paper;

‘Is the amount of board capital related to the size of the problems faced by the 50 largest financial TNCs during the 2007-2009 crisis?’

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The aim of this research is to examine if the banks could have prevented the negative impact of the financial crisis on their firm. If board capital turns out to be related to the number of problems firms faced during the crisis, the boards could have anticipated in advance. Moreover, the influence of the crisis on the development of the board capital will be explored.

This research will firstly analyze the board capital in the 50 largest financial TNCs in 2006, before the financial crisis in 2007 started. It seems reasonable that the firms with the lowest amount of board capital were facing the most problems during the crisis and performed weakest. Presumably, this poor performance has led to an intervention by the board or by the government, leading to replacement of (several members of) the board. In order to overcome the problems faced, the firms probably need a higher amount of board capital. Therefore, the conclusion seems logical; the firms with many problems during the crisis are likely to have increased board capital during or after the crisis.

This research will compare the board capital of the 50 largest financial TNCs at different points in time: in 2006, 2009 and 2011. Based on these data, it is possible to analyze if the board capital increased during or after the crisis, compared to the year before the crisis. If firms display lower board capital in 2006 compared to 2011, that could be an indication that those firms were not prepared for unexpected events. That might lead to the conclusion that if board capital in 2006 would have been higher, the impact of the crisis on the largest financial TNCs could have been less severe. Potentially more attention should then be paid to the level of board capital in future.

3.0 THEORY SECTION

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To categorize the assets of an employee and determine the contribution of those to the firm, board capital can be used. Haynes and Hillman (2010) developed two dimensions of board capital which are similar to the human and social capital theory. First, the board capital breadth (human capital) captures various heterogeneities of the board such as education, functional background, occupation, age, tenure and work experiences in other industries. Second, the board capital depth (social capital) refers to the embeddedness of the board in the focal firm’s industry, which is the result of directors’ current or former industry work experience, their horizontal or vertical ties to firms in the industry and former or current occupation or supporting service roles. 3.1 Board heterogeneity

Board capital consists of human and social capital (see above). The factors which account for board capital each show a certain level of board heterogeneity. The level of heterogeneity within a board influences the strategy pursued by a firm. Proponents of board heterogeneity argue that managers and firms benefit from directors bringing diverse social and occupational viewpoints to the boardroom. Heterogeneity can arise in different areas, such as director education, experience, profession, gender, ethnicity and age (Anderson et al., 2011). Board heterogeneity can be beneficial for firms due to the fact that directors of differing backgrounds bring varied perspectives, talents, and problem-solving skills to corporate deliberations. In line with these findings, Bantel (1993) found greater diversity in educational and functional background leads to better decision-making. This is especially beneficial for firms with complex operations, which was also confirmed by Mahadeo et al. (2012). On the other hand, high levels of board heterogeneity increase communication costs. The research by Anderson et al. (2011) confirmed that board heterogeneity is positively related to firm performance, since a diverse director pool enhances firm value. Moreover, they found that there was a positive association between firm complexity and board heterogeneity. The banking industry is a complex environment, encouraging a heterogeneous board.

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important decisions need to be taken. In order to take them quickly, unanimous and correct, a homogeneous board is beneficial.

Based on the research above, firms facing a large amount of problems during the crisis were encouraged to increase decision making and to become more homogeneous. Firms without any trouble had no incentive to make any changes. Therefore, the hypothesis with regard to homogeneity of the board is stated as follows:

Hypothesis 1: If the government intervened in a firm during the crisis, the board has become more homogeneous after the crisis.

3.2 Human capital

Board capital consists of several variables, all categorized as being human or social capital. Human capital consists of expertise, experience, knowledge, reputation and skills (Hillman and Danziel, 2003).

3.2.1 Gender

One of indicators of the level of homogeneity within a board is gender diversity. Mateos de Cabo et al. (2012) stated the poor representation of women in the highest executive positions and on the boards of European banks is generally related to the glass ceiling. One important factor that may be influencing the low presence of women on bank’s boards is the perception that they are more risk averse than men (Jiniakoplos and Bernasek, 1998; Sundén and Surette, 1998). This means that when a bank assumes a significant level of risk, it is less likely to hire women for the board, since women are seen as less skilled in making the risky decisions that may be necessary for a bank’s success (Mateos de Cabo et al. (2012). Farrell and Hersch (2005) found female board members have no influence on firm performance, whereas Vieito (2012) argued companies managed by a female CEO perform better than companies managed by a male CEO in large, medium and small sized companies. Medland (2004) found that the absence of women on boards is due to their lack of connections. Connections are an important asset in business, so the absence of connections for women might explain why Adams and Ferreira (2009) state that on average; firms perform worse the greater is the gender diversity on the board.

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3.2.2 Education

In the management literature a considerable number of studies has addressed the influence of the educational backgrounds of upper echelons (the CEO, board members and top managers) on various aspects of managerial behavior. The upper echelons theory by Hambrick and Mason (1984) is based upon two parts: (1) executives act on the basis of their personalized interpretations of the strategic situations they face, and (2) these personalized construals are a function of the executives’ experiences, values and personalities. According to the upper-echelon theory, a higher level of education is associated with open mindedness, capacity for information processing, and tolerance to changes (Hambrick and Mason, 1984). Moreover, according to Bhagat et al (2010), CEO education potentially impacts CEO ability in three mutually non-exclusive ways: (1) education could potentially contribute to the CEO’s knowledge, perspective and ability to understand technical and abstract concepts, (2) higher education could be a signal of the CEO’s intellect and ability to persevere on challenging intellectual activities and (3) the social networks acquired in college and graduate school can be quite helpful professionally in the future. All these capacities increase the potential for good performance and could be a reason for hiring an executive with a higher education. Bhagat et al. (2011) stated that the choice of CEO does not have a significant influence on long-term firm performance and that boards frequently replace a CEO with a new chief executive holding the exact same education type of the departing one. However, Aldamen et al. (2012) found evidence that expertise, the combination of education and experience, is positively related to market performance. Accordingly, the following hypothesis was formulated:

Hypothesis 3: A negative relationship exists between the average level of education on the board and the number of problems faced by the firm.

3.2.3 MBA

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zest for business’ nor the ‘will to manage’. They only seem to be willing to have power and become rich. Moreover, he added that the future managers are educated with a lack of creativity and innovation, blindness to ethical dilemmas, narcissistic behavior and an uncritical reliance on what look like data. This was confirmed by Rubin and Dierdorf (2009), stating that the MBA is wholly out-of-touch with the real world and the needs of practicing managers. Feldman (2005) mentions the litany concerning new graduates being arrogant, having no interpersonal skills and wanting to be promoted twice a year. He adds that recently, the lack of loyalty of those new graduates has increased. Slater and Dixon-Fowler (2010) divide the critics on MBA in two categories: irrelevance and a profits-first mentality. The irrelevance criticisms suggest that MBAs do not add to individual or organizational benefit since the education does not provide useful skills, knowledge or abilities for management. This was confirmed by Rubin and Dierdorf (2009) stating that especially the human capital capabilities are underrepresented in the MBA programs. They also found that there is little evidence of a connection between mastery of the MBA curriculum and subsequent on-the-job behavior. The profit-first mentality refers to the mindset of new graduates, who seek profits first, at any cost. In line with their expectations, the results of the research done by Rubin and Dierdorf (2009) showed that competencies indicated my managers to be most critical are the very competencies least represented in MBA curricula. Adding all these critics towards the MBA programs, makes it seem like MBAs do not add any value to a company and are not the best managers that a firm could get. The assumption is that recruiters hire MBAs because of the status this degree had in the past, but which is not existing anymore. The fourth hypothesis is therefore stated as follows:

Hypothesis 4: A positive relationship exists between the number of MBAs within a board and the number of problems faced by the firm.

3.3 Social capital

3.3.1 Work experience

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financial accounting knowledge, or have shorter work experience as CFOs or both. Those firms also experience more CFO turnover and are likely to hire better qualified CFOs. This is in line with Huson et al. (2004), who concluded that for companies with no good internal control just hiring a new CFO is not sufficient. The newly hired CFO needs to be better qualified. These theories led to the fifth hypothesis:

Hypothesis 5: A negative relationship exists between the average work experience within a board and the number of problems faced by the firm.

3.3.2 International experience

Besides work experience, international experience is another factor influencing the human capital. Vilet (2012) states that local nationals should be hired, not expatriates, since they lack local business experience. Business experience might have increasing importance after the crisis because firms do not want a repetition of the past and will most likely ensure the best suitable persons work for them. However, banking is a global industry, illustrated by the rapid spread of the financial crisis worldwide, which faces many risks. Sambharya (1996) mentioned two advantages of international experience for managers. First, international experience allows managers to develop a knowledge base and mindset that enables them to be more confident and effective in foreign environments. Second, managers with international experience are better equipped to deal with the uncertainties and ambiguities associated with international operations. Moreover, managers lacking international experience are less certain of their abilities to manage and control foreign operations (Cavusgil and Naor, 1987). Herrmann and Datta (2005) stated that the skill set, market knowledge and confidence associated with international experience seem to be important to competing successfully in global markets. To complement the abovementioned, Rivas (2012) stated international experience might help create opportunities that translate into better firm performance. Since the financial industry is a global industry, international experience within a board might benefit a firm. Therefore, the following hypothesis was formulated:

Hypothesis 6: A negative relationship exists between the average number of individuals with international experience on a board and the number of problems faced by a firm.

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4.0 METHODOLOGY

4.1 Sample

The United Nations Conference on Trade and Development (UNCTAD) is a conference of the United Nations and concerns trade and development. The UNCTAD was established in 1964 in order to promote economic development. In 2008, the UNCTAD published a list of the world’s largest financial TNCs in 2008. This list served as the guideline for Nieters (2012) in order to perform research on executive turnover and nationality diversity. Out of the 50 companies, two had to be excluded since the data was incomplete. Therefore, in the end a list was developed which included the other 48 companies (table 1, appendix). This list served as the main source for the dataset used in this research. It had to be complemented with more information concerning the education and experience of the board members.

4.2 Time frame

The financial crisis of 2007-2009 resulted in the threat of a total collapse of large financial institutions, the bailout of banks by national governments and downturns in stock markets around the world. This crisis is also known as the Global Financial Crisis. In order to determine what the effects of the crisis have been on the board capital of the financial institutions, the situations before and after the crisis must be compared. Therefore, 2006, 2009, and 2011 were used to obtain a complete overview of the development of the board capital within the firms during this period.

4.3 Data collection

The data collected was mainly found on the websites of the relevant companies and in their annual reports. Moreover, websites which collect data concerning individuals such as Management Scope and BusinessWeek were used.

4.3 Variables

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are gender, MBA, work experience, international experience, and level of education. The control variables included are age (year of birth), size of the TMT, total number of employees, total assets and the number of host countries.

In this research firm performance does not necessarily concern the profits made by the firms, but the other side of performance is considered: the number of problems the firm faced during the crisis. In order to measure the number of problems which the firm had to deal with, three separate variables were developed; government support, write-downs and capital raisings. If all three variables equaled zero, the firm is likely not to have had any trouble.

Dependent variable I: Government intervention

When banks are in trouble and are unable to guarantee the money of their clients, governments can intervene. One of the potential risks if the government does not help out a large bank is that a bank run will occur, which causes a loss of confidence in the financial market. This only enforces a crisis, and therefore it can be a useful decision of the government to help out the distressed banks. When a government offers money to a failing organization in order to prevent the company from bankruptcy, the government intervenes. Those ‘bailouts’ can take the form of loans, bonds, stocks or cash and the government can decide if the firms need to reimburse the money. If the government did not intervene in a financial institution, the firm will be coded with a 0. If the government did intervene in the institution, the firm is coded with a 1. In table 1 in the appendix can be found which firms received a government bailout. The amount of money received by the firms is in some of the analyses used as an independent variable as well, in those cases named ‘government support’.

Dependent variable II: Write-downs

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Dependent variable III: Capital raisings

Capital raisings are a good proxy for the extent of losses, since the firm needed to raise distressed capital. However, those capital raisings also show good prospects for the specific firm, because investors show confidence in a firm by providing capital. Consequently, capital raisings could be a sign of limited losses, which are likely to be defeated (Erkens et al., (2009). In table 1 can be found which firms had to raise capital and the amounts are provided in billion $.

Independent variable I: Gender

In many sectors, boards are required to employ a minimum percentage of women on the board. Reasons for not desiring women on the board are because they tend to be more risk averse, are less skilled in making risky decisions and because they lack connections (Jiniakoplos and Bernasek, 1998; Sundén and Surette, 1998; Mateos et al., 2011 and Medland, 2004). In order to determine whether the ratio of women within the board actually influences firm performance, women and men are coded (GEN) in the sample (men were coded with a 0 and women with a 1). Independent variable II: MBA

Many discussions debated whether or not an MBA adds value to a firm. Therefore, the education variable is further specified by determining whether the individual obtained an MBA degree. In the database individuals are categorized as either possessing no MBA (0) or an MBA degree (1). Independent variable III: Work experience

Work experience in the relevant field of business might be useful to help a firm perform well. During the crisis, many firms showed poor performance. This could be due to the lack of financial expertise as Guerrera and Larsen (2008) found within some large US financial institutions. This variable is therefore included and calculated as the average number of years work experience in the financial industry within a board (WEXP).

Independent variable IV: International experience

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them to be more confident and effective in foreign environments (Sambharya, 1996). Thus, international experience might help create opportunities that translate into better firm performance, as was found by Rivas (2012). International experience (IEXP) needs to be included in assessing board capital. Individuals without international experience are coded with 0, and individuals having international experience are coded with a 1. In order to be coded with a 1, the individual must actually have lived in a foreign country.

Independent variable V: Level of education

Goll et al. (2007) and Aldamen et al. (2012) stated education is positively related to firm performance, which explains why firms prefer to have a high level of education among the members of their boards. The education level of the board members will be analyzed by determining the highest level of education that was achieved. The level of education (ELEVEL) is divided in either not having a university degree (0), a Bachelor’s degree (1), a Master’s degree (2) and a Phd (3).

Accordingly, some control variables were included in the research in order to test the effects of certain predictors independent of the influence of others. The control variables used were: age (year of birth), the size of the TMT, the total number of employees, the total assets and the number of host countries the firm is present in.

4.4 Method of analysis

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This research is complicated since the analysis needs to be performed in two ways. Because the number of problems faced by each firm was measured only at one point in time (2007/2008), it is impossible to measure the influence of board capital after that period on the number of problems. Therefore, in 2006 will be analyzed if the board capital has had an influence on the number of problems faced. Accordingly, in 2009 and 2011 the analysis had to be reversed; the influence of the number of problems on the composition of board capital will be tested.

Starting with 2006, the three ratio data are analyzed first by means of using the linear regression method. This method fits the data, since all variables need to be ratio or interval data, and a linear relationship needs to exist between the variables. To begin with, the influence of the control variables on the dependent variable will be determined. Subsequently, one of the independent variables will be added at a time. This leads to the development of six models. The first includes solely control variables, and in the next models one of the independent variables is added. In the end, three tables will be composed this way. Subsequently, the nominal variable will be analyzed by means of the binary logistic regression. For a binary logistic regression analysis the dependent variable needs to contain two possibilities: yes or no (value 0 or 1). Like with the other dependent variables, first the control variables are implemented in the model, followed by the independent variables.

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capital. Firms which had few problems will most likely not have experienced significant changes.

According to the hypotheses, it is expected that firms facing many problems have a higher number of MBAs on the board, lower education levels and less experienced individuals. Shortly, they are expected to have lower board capital than well performing firms. Here, the two groups can be compared in order to determine whether this hypothesis can be confirmed. It will show whether or not the poorly and well performing firms have recruited a different type of individual during the crisis, and have therefore increased the amount of board capital. It would be expected that the poorly performing firms show an increase in board capital, whereas the well performing firms might have the same amount of board capital. Moreover, the extent to which the boards are heterogeneous can be deducted. It is expected that boards facing problem will increasingly hire individuals which fit within the organization and create a more homogeneous board in order to for example enable quick decision making.

5.0 RESULTS

5.1 Description of the data

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information regarding their education and their experience in the financial industry has been collected. In the descriptive statistics in table 2 can be found that the mean work experience (in the financial industry) of all board members was 19,05 years, thirteen percent of the board members were female, eighteen percent of the board members had international experience, the mean education level was 1,72, which is in between a bachelor’s degree (1) and a master’s degree (2). Moreover, twenty percent of the participants had obtained an MBA degree. These are the most important data for the research. In table 3 in the appendix the descriptive statistics of each separate year of analysis can be found.

Table 2: Descriptive statistics entire dataset

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

Gender 1632 0 1 ,13 ,337 Year of birth 1632 1924 1982 1951,43 8,530 Work experience 1572 1 60 19,05 12,297 International experience 1632 0 1 ,18 ,388 Level of education 1336 0 3 1,72 ,908 MBA 1341 0 1 ,20 ,399 Valid N (listwise) 1255 5.2 Correlation

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Table 4: Correlation matrix (Pearson correlation)

*. Correlation is significant at 0.10 level (two-tailed) **. Correlation is significant at 0.05 level (two-tailed) ***. Correlation is significant at 0.01 level (two-tailed)

1 2 3 4 5 6 7 8 9 10 11 12 1 Gender 2 Work experience -,291** 3 International experience -,146 ,037 4 Education level ,079 ,100 ,225 5 MBA ,339** ,099 -,063 ,422*** 6 Government intervention -,114 -,376*** ,091 ,007 -,101 7 Write-downs -,115 -,047 ,250* ,189 ,145 ,579*** 8Capital raisings -,143 ,087 ,181 ,197 ,325** ,371*** ,708***

9 Amount of gov. support -,148 ,066 ,141 ,115 ,323** ,403*** ,674*** ,951***

10 Total assets -,044 ,015 ,037 -,095 ,074 ,192 ,249 ,267* ,302**

11 Total employees -,074 ,179 ,075 ,127 ,228 ,021 ,177 ,394*** ,428*** ,604***

12 Average board size ,080 -,068 -,141 ,022 ,178 ,075 -,003 ,267* ,267 ,309** ,232

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The coefficients indicate that gender is negatively related to work experience and positively related to MBAs. Work experience is also negatively related to government intervention. MBAs are positively related to the education level, capital raisings and the amount of government support. Government intervention is positively related to write-downs, capital raisings and the amount of government support, which is essential for our measure of the number of problems, based on these variables. Capital raisings are positively related to the number of employees. The total assets are positively related to the amount of government support, as well as to the total number of employees and the average board size. At last, the year of birth is negatively related to work experience and to total employees.

5.3 Collinearity

Besides the correlation, the independent variables were checked for collinearity. When a perfect linear relationship exists among the independent variables, the estimates for a regression model cannot be uniquely computed. First, the collinearity analysis is SPSS is used to produce a Tolerance value, which is an indication of the percentage of variance in the independent variable that cannot be accounted for by the other predictors. This value should not be smaller than 0.10. Second, the Variance Inflation Factor (VIF) should not exceed a value of 10. If it does, collinearity is certainly the case. For the first regressions, where the number of problems faced by the firms is used as the dependent variable, the collinearity statistics in table 5 (see appendix) are causing no issues. However, in the second part of table 5, where MBA (1) is the dependent variable can be seen that ‘capital raisings’ and ‘the amount of government support’ have VIF values of above 10. For that reason, those two variable cannot be used in one model. Therefore, removing the amount of government support from the analysis results in no issues for the collinearity statistics, as stated in table 5, where MBA (2) is the dependent variable. Concluding, in the analyses in this research, the independent variables ‘capital raisings’ and ‘amount of government support’ will not be put in a model simultaneously.

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three dependent variables could be analyzed by means of a linear regression. First, the influence of the control variables on the dependent variable was tested, and consequently one independent variable was added each time, resulting in six models per dependent variable.

In order to determine whether the data fit the model, for the logistic regression the R square and for the binary regression model the Hosmer-Lemeshow results can be used. The R square is a statistical measure of how close the data are to the fitted regression line; it is the percentage of the response variable variation that is explained by the linear model and in between 0-100 percent. The higher the percentage, the better the model fits the data. The Hosmer and Lemeshow's goodness of fit test divides subjects into deciles based on predicted probabilities, and then computes a chi-square from observed and expected frequencies. A p-value is computed from the chi-square distribution to test the fit of the logistic model. A test statistic greater than 0.05 is desired, as wanted for all well-fitting models. That is, well-fitting models show non-significance on the goodness-of-fit test, indicating model prediction that is not significantly different from observed values.

In the subsequent tables can be found that the more variables are added, the higher the R square becomes. This indicates that the variables included are relevant for the tests conducted. Moreover, the Hosmer-Lemeshow value increases as more variables are put in the model too, indicating a better fit model.

5.4.1 Control variables

The only control variable showing significant relationships with the dependent variable is the number of host countries a firm is present in (table 6, 7 and 9). In almost all models the number of host countries displays a relationship with a p-value smaller than 0.10 or 0.05 with write-downs and capital raisings. Both relationships display a positive Beta, indicating that presence in more host countries leads to higher write-downs and capital raisings. This allows for the conclusion that internationalization is not necessarily beneficial to firms in the banking industry.

5.4.2 Human capital

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0.10 significance level. For both write-downs and capital raisings a positive relationship exists, indicating that a higher number of MBAs within the board leads to more write-downs and more capital raisings. This might be contradictive to what would be expected, since a high education level would seem to be beneficial to the firm. However, in the existing literature (Mintzberg, 2004; Rubin and Dierdorf, 2009, Feldman, 2005), researchers already hypothesized the negative impact of MBAs on performance.

Since MBAs are not significantly related to government intervention, hypothesis 4; MBAs are positively related to the number of problems faced by the firms, can only partly be accepted. The relationship is negative, which was stated in the hypothesis as well.

Since gender, the level of education and international experience do not show any significant relationship, hypothesis 2, 3 and 6 can be rejected: Gender, the level of education and international experience are not (negatively) related to the number of problems faced by a firm.

5.4.3 Social capital

Work experience in the financial industry is categorized as social capital. Work experience solely has a significant influence on government intervention. A significant influence at the 0.05 significance level can be found, with a negative Beta. Therefore, more work experience would lead to lower chance on government support. The salient fact is that work experience is not significantly related to the amount of government support. This would lead to the conclusion that many years of work experience are helpful in preventing government intervention, but do not make any difference in the amount of support needed. Thus, there would supposedly be an absolute level of work experience needed in order to prevent the intervention but it is irrelevant to which degree this absolute level would not, or would be reached.

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Table 6: Write-downs 2006 Dependent variable: Write-downs

Independent variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Year of birth (B) (t-value) 0.058 0.385 0.046 0.277 -0.028 -0.166 -0.079 -0.455 -0.098 -0.540 -0.085 -0.457 Size TMT 0.127 0.852 0.124 0.824 0.097 0.663 0.109 0.742 0.130 0.839 0.117 0.721 Total employees 0.155 0.682 0.140 0.570 0.008 0.034 0.009 0.035 0.021 0.085 0.028 0.111 Total assets -.033 -0.186 -0.024 -0.127 0.039 0.212 0.021 0.114 0.021 0.114 0.031 0.162

Number host countries 0.344*

1.712 0.356 1.663 0.391* 1.883 0.409* 1.959 0.376* 1.696 0.367 1.627 Gender 0.028 0.178 -0.059 -0.374 -0.101 -0.620 -0.091 -0.548 -0.099 -0.581 MBA 0.295* 1.939 0.349** 2.158 0.341** 2.075 0.318* 1.785 Work experience -0.152 -0.989 -0.158 -1.017 -0.155 -0.980 International experience 0.071 0.476 0.054 0.340 Education level 0.057 0.348 R square 0.255 0.255 0.319 0.336 0.340 0.342 Significance 0.026 0.049 0.023 0.029 0.046 0.073

Table 7: Capital raisings 2006 Dependent variable: Capital raisings

Year of birth (B) (t-value) 0.052 0.353 0.053 0.323 -0.015 -0.092 -0.073 -0.428 -0.079 -0.448 -0.085 -0.467 Size TMT 0.107 0.733 0.107 0.723 0.082 0.566 0.095 0.660 0.103 0.673 0.109 0.685 Total employees 0.158 0.711 0.159 0.662 0.038 0.157 0.038 0.158 0.043 0.173 0.039 0.157 Total assets -0.007 -0.041 -0.007 -0.041 0.050 0.278 0.030 0.166 0.030 0.164 0.025 0.136

Number host countries 0.370*

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Table 9: Government intervention

Dependent variable: Government intervention (yes / no)

Year of birth (B) 0.044 0.085 0.090 0.025 0.008 0.016

Size TMT -0.011 -0.009 -.008 -0.001 0.014 0.008

Total employees 0.000 0.000 0.000 0.000 0.000 0.000

Total assets 0.000 0.000 0.000 0.000 0.000 0.000

Number host countries 0.047 0.039 0.039 0.053 0.046 0.045

Gender -3.300 -3.125 -6.218 -5.790 -6.031

MBA -0.408 2.305 1.933 1.575

Work experience -0.231** -0.247** -0.246**

International experience 2.408 0.290

Education level -28.015

Hosmer & Lemeshow 0.227 0.166 0.626 0.698 0.328 0.951

5.5 The influence of the number of problems faced by firms on board capital (2009)

By means of logistic regression analyses and another stepwise model can be analyzed if the problems faced by the firms in 2007/2008 had an influence on the composition of the board in 2009 and 2011. First, a model is used where only the control variables were inserted, followed by one of the independent variables at a time. The sequence of adding the independent variables was done by means of occurrence in a firm. When a firms faces problems, write-downs will occur on their assets first. If this is insufficient, the firm will accordingly raise capital. The last resort is when the government needs to intervene, but this will be prevented as long as possible.

First, a model was tested in which solely the control variables were included. Accordingly, the indicators of problems for a firm were included. As mentioned in the section concerning collinearity, the ‘amount of government support’ will be left out in those models.

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capital is international experience. The year of birth positively influences international experience (table 13). So, the younger board members are, the more international experience they have. Probably this is due to the relatively new trend of studying and/or working abroad, since the possibilities have increased extremely over the last couple of years. The number of host countries also positively influenced international experience, mostly at with a p-value smaller than 0.01. It makes sense that the more foreign subsidiaries a firm has, the more employees are encouraged to work abroad for at least a while.

Work experience is negatively influenced by the year of birth, so the older people are, the more relevant work experience they possess (table 12). Another finding is that government intervention negatively impacts work experience, so if the government intervened, less work experience is found within a board (p-value < 0.10). It is likely that the government encourages the firms to hire young board members in order to turn things around.

Table 10: Gender 2009

Dependent variable: Gender (2009)

Independent variables Model 1 Model 2 Model 3 Model 4

Year of birth (B) (t-value) 0.547*** 0.4142 0.560*** 4.185 0.553*** 4.066 0.554*** 4.029 Size TMT 0.151 1.164 0.164 1.245 0.165 1.241 0.165 1.230 Total employees 0.542** 2.663 0.562*** 2.726 0.561** 2.693 0.538** 2.484 Total assets -0.381** -2.437 -0.388** -2.464 -0.382** -2.394 -0.368** -2.238

Number host countries -0.551***

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Table 11: MBA 2009

Dependent variable: MBA (2009)

Year of birth (B) (t-value) 0.144 0.886 0.098 0.624 0.110 0.689 0.112 0.704 Size TMT 0.173 1.076 0.129 0.834 0.127 0.816 0.128 0.822 Total employees 0.503* 2.004 0.433* 1.786 0.435* 1.782 0.367 1.463 Total assets -0.221 -1.147 -0.198 -1.070 -0.208 -1.112 -0.165 -0.870

Number host countries -0.192

-0.889 -0.312 -1.454 -0.329 -1.512 -0.311 -1.434 Write-downs 0.352** 2.178 0.072 0.157 0.050 0.110 Capital raisings 0.305 0.652 0.406 0.856 Government intervention -0.181 -1.135 R-square 0.120 0.211 0.220 0.245 Significance 0.351 0.117 0.161 0.163

Table 12: Work experience 2009 Dependent variable: Work experience (2009)

Year of birth (B) (t-value) -0.436*** -3.063 -0.483*** -3.166 -0.491*** -3.164 -0.487*** -3.232 Size TMT 0.040 0.268 0.021 0.140 0.022 0.147 0.023 0.155 Total employees -0.031 -0.134 -0.062 -0.262 -0.063 -0.265 -0.166 -0.699 Total assets 0.010 0.056 0.020 0.112 0.027 0.147 0.091 0.504

Number host countries 0.152

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Table 13: International experience 2009 Dependent variable: International experience (2009)

Year of birth (B) (t-value) 0.543*** 4.055 0.547*** 3.999 0.524*** 3.865 0.524*** 3.816 Size TMT -0.243* -1.841 -0.240* -1.779 -0.236* -1.778 -0.236* -1.756 Total employees 0.056 0.274 0.062 0.295 0.058 0.279 0.062 0.287 Total assets -0.100 -0.633 -0.102 -0.636 -0.083 -0.522 -0.086 -0.521

Number host countries 0.487***

2.736 0.497** 2.666 0.530*** 2.868 0.529*** 2.819 Write-downs -0.028 -0.201 0.519 1.331 0.521 1.317 Capital raisings -0.597 -1.501 -0.603 -1.471 Government intervention 0.011 0.082 R-square 0.404 0.404 0.436 0.436 Significance 0.000 0.001 0.001 0.002

5.6 The influence of the number of problems faced by firms on board capital (2011)

By performing the same analyses for 2011 as for 2009, the effect of the hiring policies can be measured after a longer period, so the effect becomes more obvious. Probably the results for 2011 will show more significant relationship and those are likely to be more reliable.

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The variable categorized in this section, work experience, is in table 17 negatively impacted by government intervention (p-value < 0.10). The relationship is not very strong, but government intervention would result in lower work experience levels, probably caused by younger employees (see 2009).

Table 15: Gender 2011

Dependent variable: Gender (2011)

Year of birth (B) (t-value) 0.350* 1.937 0.387** 2.133 0.387** 2.101 0.388** 2.070 Size TMT 0.044 0.285 0.055 0.361 0.054 0.352 0.056 0.355 Total employees 0.410 1.443 0.470 1.645 0.472 1.624 0.477 1.585 Total assets -0.063 -0.321 -0.078 -0.400 -0.077 -0.393 -0.081 -0.395

Number host countries -0.497**

-2.180 -0.433* -1.868 -0.431* -1.835 -0.433* -1.812 Write-downs -0.212 -1.287 -0.178 -0.380 -0.177 -0.373 Capital raisings -0.038 -0.078 -0.044 -0.089 Government intervention 0.013 0.075 R-square 0.139 0.173 0.173 0.173 Significance 0.258 0.227 0.328 0.437 Table 16: MBA 2011

Year of birth (B) (t-value) 0.234 1.236 0.175 0.943 0.107 0.643 0.112 0.663 Size TMT 0.057 0.353 0.039 0.251 0.060 0.434 0.048 0.339 Total employees 0.369 1.239 0.275 0.940 0.187 0.713 0.151 0.559 Total assets -0.216 -1.052 -0.192 -0.967 -0.208 -1.169 -0.182 -0.992

Number host countries -0.122

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Table 17: Work experience 2011 Dependent variable: Work experience (2011)

Year of birth (B) (t-value) -0.280 -1.488 -0.309 -1.620 -0.328 -1.705 -0.314 -1.685 Size TMT -0.040 -0.250 -0.049 -0.304 -0.042 -0.264 -0.084 -0.538 Total employees -0.073 -0.245 -0.119 -0.397 -0.145 -0.478 -0.266 -0.888 Total assets 0.038 0.188 0.050 0.244 0.045 0.221 0.132 0.648

Number host countries 0.056

0.235 0.006 0.024 -0.009 -0.037 0.026 0.110 Write-downs 0.165 0.955 -0.221 -0.454 -0.249 -0.529 Capital raisings 0.426 0.849 0.597 1.209 Government intervention -0.324* -1.941 R-square 0.068 0.089 0.105 0.184 Significance 0.688 0.679 0.697 0.387

5.7 Board capital over time

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Table 20: Ranges of firms with government support

Government support _Range _Mean

N 2006 2009 2011 2006 2009 2011 Gender 20 ,25 ,25 ,25 ,0842 ,1103 ,1341 Age 20 8,54 9,23 7,16 56,89 57,36 57,42 Work experience 20 11,86 11,05 18,83 18,2043 18,4644 18,8111 International experience 20 ,38 ,47 ,96 ,1878 ,1885 ,2340 Education level 20 1,58 1,78 2,53 1,7216 1,7596 1,7469 MBA 20 ,35 ,43 1,00 ,1685 ,1767 ,2309 Proportion foreigners 20 55,00 70,83 60,87 22,0575 22,6745 20,7895 Size TMT 20 19,00 21,00 18,00 22,0000 22,0000 21,3500 Valid N (listwise) 20

The first variable gender provides an insight on the development of gender diversity over the years. The mean has increased, indicating more women were on the board in 2011 compared to 2006 (increase of 3,99 percent point). This means the boards have become more gender diverse, indicating an increase in heterogeneity. The range of gender has remained equal, so the difference between the minimum and maximum number of women on a board has not changed. The age of board members has hardly changed either. The average work experience has increased, but the range has increased as well. International experience has increased a lot, as well as the range. This could be due to the fact that it is more common now to have worked abroad than it was in the past. Education level has not really changed (increased and decreased) but the range has increased a lot, which indicates larger differences between board members. The number of MBAs has increased a lot, by 6,24 percent point, potentially since firms assume those graduates can help them out since they enjoyed a high education. The number of foreign board members has in the end decreased, maybe due to the government choosing some of the new board members.

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Table 21: Government intervention statistics No government support

(N=28)

Government support (N=20) Entire database (N=48)

2006 2009 2011 2006 2009 2011 2006 2009 2011 Gender 0,112 0,139 0,146 0,084 0,110 0,134 0,101 0,127 0,141 Age 57,60 58,15 58,83 56,89 57,36 57,42 57,30 57,82 58,24 Work experience 20,247 19,928 20,457 18,204 18,464 18,811 19,396 19,318 19,771 International experience 0,150 0,172 0,188 0,188 0,189 0,234 0,166 0,179 0,207 Education level 1,694 1,686 1,681 1,722 1,760 1,747 1,706 1,717 1,709 MBA 0,186 0,188 0,201 0,169 0,177 0,231 0,179 0,183 0,213 Proportion of foreigners (%) 23,797 26,760 26,451 22,058 22,675 20,790 23,072 25,058 24,092 TMT size 21,143 20,393 21,357 22,000 22,000 21,350 21,500 21,063 21,354 Total assets 957106 957106 957106 1305075 1305075 1305075 1102093 1102093 1102093 Total employees 87957,0 87957,0 87957,0 91373,7 91373,7 91373,7 89380,6 89380,6 89380,6 Number of host countries 27,393 27,393 27,393 32,150 32,150 32,150 23,375 23,375 23,375 Write downs 3,596 3,596 3,596 15,860 15,860 15,860 8,706 8,706 8,706 Capital raisings 3,571 3,571 3,571 16,660 16,660 16,660 9,025 9,025 9,025 Government support 0,000 0,000 0,000 24,196 24,196 24,196 10,082 10,082 10,082

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caused the difference. It is likely that the government wants the firm to hire national instead of foreigners. Again, making statements concerning the development of the board capital is difficult, but the four main indicators of board capital can be assessed. In the period between 2006 and 2011, the firm where the government intervened possessed increasingly higher levels of international experience and education. At last, they used to have a lower number of MBAs on the board than on average, but they had a higher number of MBAs on their boards in 2011. Therefore, those firms did increase their board capital.

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Table 22: MBA dispersion

Company country of origin

Frequency Percent Percentage

MBAs on board Total percentage per country Weighted average percentage Switzerland 4 8.3 22 182.6 9.3 United States 6 12.5 36 450 22.9 Netherlands 3 6.3 16 100.8 5.1 Canada 3 6.3 34 214.2 10.9 France 5 10.4 8 83.2 4.2 United Kingdom 6 12.5 25 312.5 15.9 Germany 4 8.3 17 141.1 7.2 Japan 3 6.3 10 63.0 3.2 Ireland 1 2.1 13 27.3 1.4 Spain 2 4.2 6 25.2 1.3 Italy 3 6.3 10 63.0 3.2 Sweden 3 6.3 20 126.0 6.5 Belgium 3 6.3 11 69.3 3.5 Norway 1 2.1 25 52.5 2.7 Denmark 1 2.1 25 52.5 2.7 Total 48 100.0 1963.2 100.0 Average 18.5 6.67

6.0 DISCUSSION AND CONCLUSION

The aim of this research was to find out whether board capital and the problems faced by the 50 largest financial TNCs during the financial crisis were related. First, it was examined if board capital was related to the number of problems faced. Second, it was studied if the problems faced during the crisis affected the board capital after the crisis. In order to execute this research, data regarding the composition of the boards of the firms in 2006, 2009 and 2011 was collected. 6.1 Discussion

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only MBAs and work experience had a strong and significant influence on the problems faced by the firms. MBAs were found to positively influence capital raisings and write-downs. It is likely that the boards of the firms would not expect this relationship, since MBAs enjoyed a high level of education and are often praised for their managerial skills, like Baruch and Leeming (2001) mentioned. Therefore, this finding adds to the existing literature by being contradictive with some literature, although some researchers were skeptical about the value of an MBA degree (Mintzberg, 2004). Moreover, work experience proved to be negatively related to government experience. Thus, more work experience diminishes the chance of needing government intervention. However, work experience did not show any relationship with the amount of government support, so it only affects the chance of government intervention, not the extent. This first analysis showed that only two out of the five variables which are part of board capital are significantly related to at least one of the dependent variables indicating the problems of the financial firms. Moreover, MBAs showed a positive relationship, whereas work experience was negatively related to the problems.

The concept of board capital could in this analysis be questioned, since variables revealed both positive and negative relationships with the independent variables. In order to consider board capital as a reliable concept, all variables which create that concept should show similar relationships with independent variables. Therefore, all relationships found should be either positive or negative. An increase in board capital would thus mean the values of all variables have increased. Here, MBAs should decrease due to the positive relationship with problems, and the other variables (such as education level) should increase to increase board capital. This is inconsistent and therefore the value of the concept of board capital should be doubted.

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results seem contradicting, since write-downs and capital raisings are positively correlated. An explanation for these divergent findings has not been found yet. Lastly, government intervention was again negatively influencing work experience in 2011. Concluding, basically the same results were found for 2009 and 2011, but the relationships in 2011 were more significant. This could be due to the time it takes to execute decisions made. In 2009 the crisis was felt severely in some of the firms and they knew changes had to be made. However, it takes time to decide which board members need to be replaced and to find a suitable replacement. Therefore, in 2011 the results of the crisis in terms of board capital changes might be more evident.

The second part of the research aimed to analyze the development of diversity within the board. The firms with and without government intervention were compared as being two different groups (table 21). Comparing the ranges of the variables of the two groups led to no striking differences. Moreover, the ranges of the variables of the firms experiencing government intervention often increased (table 20) which indicates an increase of heterogeneity. Upudhyay (2012) stated a more diverse board slows down the process of decision making, which is important when facing problems. Therefore, a more homogeneous board would be the logical result of a period of problems. However, Bantel (1993) found greater diversity in education leads to better decision making. The education diversity did increase for the firms with government intervention, which might lead to better results. The reason for the rejection of the hypothesis could maybe be found in the interference by the government. When intervening, they also take part in the decisions made. Accordingly, some decisions made could be contradicting the decisions the firm would make without any saying of the government.

6.2 Limitations and future research

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dependent variables as well. For example, at first board independence was intended to be included as well. However, for many board members it was hard to find if they were inside or outside members, allowing for invalid results. Fifth, it will be interesting to see if the results found in this paper will also last in the longer term. Redoing this analysis in a couple of years will tell if the value of an MBA degree is seriously questionable. At last, since this research provided some new insights into the value of an MBA degree, a study purely focusing on the value of MBA degrees might be providing some very interesting results. For example, since MBAs positively influence capital raisings and capital raisings positively influence MBAs on a board, a more in depth research on this relationship could help firms determine whether or not to hire MBAs. Another interesting aspect to study more in depth could be to differentiate between countries to a higher level. As was showed in table 22, large differences between countries exist with regard to the proportion of MBAs on boards. Grouping countries with high and low numbers of MBAs and comparing performance could provide interesting results.

6.2 Conclusion

The research question which guided this paper was: ‘Is the amount of board capital related to the size of the problems faced by the 50 largest financial TNCs during the 2007-2009 crisis?’ By means of assessing the board capital of the firms included in the research as well as the problems they faced, measured as write-downs, capital raisings and government intervention, a good view on each firm was obtained.

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the banking industry and more than half had no financial service experience at all. According to the results of this paper, this would have led to an increased chance of government intervention. Hypothesis 2, 3 and 6 had to be rejected since gender, education level and international experience showed no significant relationships with one of the indicators of the problems. Hypothesis 4 concerning MBAs was accepted, since for two of the indicators concerning problems a significant relationship was found. Thus; ‘A positive relationship exists between the number of MBAs within a board and the number of problems faced by the firm’. Hypothesis 5 was related to the work experience in a board. This hypothesis could be accepted only for government intervention, thus for one of the indicators. ‘A negative relationship exists between the average work experience within a board and the number of problems faced by the firm.’ The last part should be replaced by ‘... and the chance of government intervention’ in order to be accepted. The last hypothesis to be discussed is hypothesis 1, concerning the development of homogeneity within the board. This hypothesis had to be rejected, since no overall decrease of diversity could be found.

The second analysis proved that capital raisings and write-downs significantly influence the number of MBAs on a board in 2011 and government intervention negatively affects the work experience in a board. It seems that firms could end up in a vicious circle, since MBAs positively influence capital raisings and capital raisings positively influence MBAs on a board. The negative influence of write-downs on MBAs could not be explained yet.

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7.0 REFERENCES

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8.0 APPENDICES

APPENDIX 1

Table 1: List of the companies

Government support Write-downs (billion $) Capital raising (billion $)

1 ABN Amro Yes 2.3 0

2 Aegon NV Yes 2.7 1

3 Allianz SE No 4.1 0

4 American International Group Inc. Yes 90.8 82.2

5 Aviva PLC No 0.5 0

6 Axa No 3.8 3

7 Banco Santander SA No 1.1 0

8 Bank Of Ireland PLC Yes 5.2 4.4

9 Barclays PLC No 9.1 18.6

10 BBV Argentaria SA No 1 0

11 Berkshire Hathaway Inc No 0.8 0

12 BNP Paribas Yes 4 6.3

13 Citigroup Inc Yes 60.8 71.1

14 Crédit Agricole SA Yes 8.8 8.5

15 Credit Suisse Group AG No 10.5 11.6

16 Danske Bank A/S Yes 2 0

17 Deutsche Bank AG No 10.8 6.1

18 Dexia Yes 1.6 0

19 DNB Nor ASA No 1.7 2.4

20 Fortis NV Yes 7.4 23.1

21 Generali Spa No 2 0

22 Goldman Sachs Group Inc Yes 4.9 10.6

23 HSBC Holdings PLC No 27.4 21.6

24 Hypo Real Estate Holding Yes 3.6 0

25 ING Groep NV Yes 6.7 4.8

26 Intesa Sanpaolo No 0.7 2.2

27 JPMorgan Chase & Company Yes 18.8 19.7

28 KBC Group NV Yes 10.7 5.7

29 Manulife Financial Corp. No 2.4 4.7

30 Mitsubishi UFJ Financial Group No 1.6 4.5

31 Mizuho Financial Group Inc No 6.1 5.7

32 Morgan Stanley Yes 15.7 14.6

33 Muenchener Rueckversicherung AG No 0.6 0

34 Natixis Yes 5.3 11.8

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36 Nordea Bank AB No 1 3.3

37 Prudential PLC No 2 0

38 Royal Bank Of Scotland Yes 14.9 24.3

39 Skandinaviska Enskilda Banken AB No 0.3 0

40 Société Générale Yes 6.8 16.8

41 Standard Chartered PLC No 0.5 1.5

42 Svenska Handelsbanken AB No 0.5 0

43 Swiss Reinsurance Company No 1.7 2.6

44 The Bank of Nova Scotia No 1.5 0

45 The Royal Bank of Canada No 2.2 0

46 UBS AG Yes 44.2 28.3

47 Unicredito Italiano Spa No 2.8 5.4

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APPENDIX 2

Table 3: Descriptive statistics 2006, 2009 and 2011

Entire database (N=48) Variables 2006 2009 2011 Gender 0,101 0,127 0,141 Year of birth 1948,697 1951,180 1952,761 Work experience 19,396 19,318 19,771 International experience 0,166 0,179 0,207 Education level 1,706 1,717 1,709 MBA 0,179 0,183 0,213 Proportion of foreigners 23,072 25,058 24,092 TMT size 21,500 21,063 21,354 Total assets 1102092,958 1102092,958 1102092,958 Total employees 89380,625 89380,625 89380,625

Number of host countries 23,375 23,375 23,375

Write downs 8,706 8,706 8,706

Capital raisings 9,025 9,025 9,025

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APPENDIX 3

Table 5: Collinearity statistics

Dependent variable: Write-downs

Tolerance VIF

Year of birth 0.500 2.002

Average board size 0.821 1.218

Total employees 0.390 2.567 Total assets 0.497 2.011 Gender 0.724 1.381 MBA 0.623 1.604 Work experience 0.791 1.381 International experience 0.708 1.413 Education level 0.694 1.441

Write-downs 0.380 2.629

Capital raisings 0.084 11.952

Amount of government support 0.092 10.895

Government intervention 0.637 1.569

Write-downs 0.383 2.608

Capital raisings 0.497 2.013

‘The influence of board capital on the number of problems financial firms faced during the 2007-2009 financial crisis’

‘The influence of board capital on the number of problems financial

firms faced during the 2007-2009 financial crisis’

CONTENTS