NIVELES DE RESPONSABILIDAD PARA LA ADMINISTRACIÓN Y

In order to test the hypotheses, an ordinary least squares regression analysis is performed. The regression analysis is divided into two separate analyses. The first analysis comprises ROA and LnTQ, respectively, tested with data of the BOD. The second analysis comprises ROA and LnTQ, respectively, tested with data of the BOM.

5.4.1 OLS using BOD data

In the first step the control variables are put into the analysis, with the dependent variable ROA. Secondly the diversity variables GD and AD are added to the analysis (see table 5, column 1). Apparently, in column 1, no control variable is highly significant at the 0.01 level. LnLIQ is positive and significantly related to ROA at the 0.05 level. This result could be explained that both variables calculate the soundness of a firm. If a firm is well performing, it is suggested that the firm’s ability to pay off its debt in the upcoming 12 months is sound (Miller & Triana, 2009).

Table 5: OLS Regression analysis based on BOD data testing GD and AD

Hypothesis 1 predicts that gender diversity influences financial firm performance (ROA) positively. Column 1 tests hypothesis 1 by adding gender diversity to the regression. Based on the results from

column 1 in table 5, hypothesis 1 does not find any support for BOD. In case the coefficients were significant, ROA would be 2.3% higher with female representation. For example, if the average percentage of female representation in a boardroom grows from 6.255% to 12%, ROA with an average of 5.079 would become 5.196 in roughly the midpoint of this dataset. Similar to GD, the age diversity variable is added in column 1. Hypothesis 2 predicts that diversity of age positively influences financial firm performance (ROA). The results suggest that age diversity is negative, but insignificantly related to ROA. ROA would be 4% higher if AD would decrease. For example, if the average AD (99.920) would decrease by 4% to 95.920, mean ROA would increase from 5.079 to 5.282. In this sample of 95 Dutch listed firms, gender and age diversity does not influence ROA significantly, and therefore hypothesis 1 and 2 does not find any support based on the accounting measure of performance.

In column 2 from table 5, LnTQ is used as dependent variable in order to test the hypotheses with the gender and age diversity variables. Apparently, in comparison to ROA, the control variables FA and LnBS become significant. FA is negative and significantly related to LnTQ, suggesting LnTQ to be higher for younger firms. This result is also found by Marinova et al. (2010).

Column 2 also indicates that LnTQ is positively correlated with LnBS and Liquidity. Despite the unknown direction of causality, this may reflect that firms could improve their Tobin’s Q, and their liquidity, by increasing the number of board members. This result supports the theory of Van den Berghe & Levrau (2004), who argues that increasing the boardroom provides an increased pool of expertise, and eventually firm performance.

For the gender diversity variable, the results in column 2 suggest that GD does not influence LnTQ significantly. TQ7 would be 0.5% higher with female representation. For example, if the average percentage of female representation in a boardroom grows from 6.255% to 12%, TQ with an average of 1.087 would become 1.092 in roughly the midpoint of this dataset.

For the age diversity variable, column 2 reports a negative but statistically insignificant relationship between age diversity and LnTQ. TQ would be 0.3% higher if AD would decrease. For example, if the average AD (99.920) would decrease by 0.3% to 99.620, mean TQ would increase from 5.079 to 5.094. In line with the results of the accounting based performance measure ROA, the market based performance measure LnTQ does not seems to be significantly related to board diversity. This entails that in this sample of BOD data from 95 Dutch listed firms, no support exists for hypothesis 1 and 2 which states that board diversity positively influences financial firm performance.

5.4.2 OLS using BOM data

The following regression analysis (see table 6) is similar to the previous one; the main difference is the use of data. For this analysis the data set of the board of managers is used.

In the first step the control variables are put into the analysis, with ROA as the dependent variable. Secondly the diversity variables BOM_GD and BOM_AD are added to the analysis (see table 6, column 1). Obviously, for BOM data no control variable is highly significant at the 0.01 level. Similar to the regression analysis in table 5 with BOD data, LnLIQ is positive and significantly related to ROA at the 0.05 level. A sounder firm is supposed to more liquid assets to pay off debts (Miller & Triana, 2009). Based on the results from column 1 in table 6, gender and age diversity for BOM seems to be negative, but again statistically insignificant related to ROA. For this reason hypothesis 1 and 2 does

7

Due to the use of natural logarithm for Tobin’s Q, the coefficients from the independent variables should be interpreted as approximate percentage changes in the underlying dependent variables (Dezso & Ross, 2012).

not find any support for the board of management data. Obviously, the results suggest that there is no difference between BOD and BOM data, testing board diversity on the accounting based performance measure ROA.

In column 2 from table 6, LnTQ is used as dependent variable in order to test the hypotheses with the gender and age diversity variables from the board of management. Similar to the results from table 5, firm age becomes, in contrast with ROA, statistically significant related to LnTQ. As mentioned before, the negative and significant relation suggests LnTQ to be higher for younger firms (Marinova et al., 2010).

Table 6: OLS regression analysis based on BOM data testing BOM_GD and BOM_AD

For the gender diversity variable BOM_GD, the results in column 2 suggest that BOM_GD does not influence LnTQ significantly. According to the age diversity variable BOM_AD, column 2 reports a negative but statistically insignificant relationship between age diversity and LnTQ. Following the insignificant results of both diversity variables, hypothesis 1 and 2 find, once again, no support in this sample for the board of management. Obviously, in contrast with my predictions, there appears to be no statistical significant difference between the data of BOD and BOM in testing board diversity on financial firm performance.

5.4.3 Robustness check of results

In line with prior research (Dezso & Ross, 2012) I would like to test if the results of the main regression analyses are consistent, robust and provide reliable outcomes. In the main regression analyses gender diversity is used as a percentage of women present for the BOD and BOM data. Due the fact that some firms in our data sample have a BOD (and BOM) with more than one woman, I chose to use a dummy gender diversity variable in order to check the robustness of results. As well as for the age diversity variable, instead of using only the age diversity scale (Siciliano, 1990), the robustness of results is checked through the use of a variable which is defined as the average age of the directors in the BOD (and BOM). Similar to the main regression analyses, I split up the analysis in use of BOD/BOM data.

5.4.3.1 OLS robustness check using BOD data

Through the use of the other two diversity variables DGD and AA, I repeated the regression analysis from table 5. The DGD and AA columns in table 7 reports an excerpt of the results (complete results are attached in the appendix), in comparison to the regression analysis results of GD and AD from table 5.

Table 7: Excerpt of the regression analyses to reveal the differences between GD, DGD, and AD, AA

Table 7 clearly indicates that the result from GD on ROA (0.023) is consistent and robust, due to the almost similar results of DGD (0.107). In using LnTQ as the dependent variable, DGD appears to be, in contrast with GD (0.005), negatively related to LnTQ (-0.062). Due to the minor difference (DG to LnTQ is almost zero) and the insignificant relation of both variables, I accept and believe that the results are consistent and robust. In the yellow and purple part, AA appears to be positive related to ROA (LnTQ) which is in contrast to the results of AD. Due to the insignificance of the variables I accept and believe that the results are consistent and robust, meaning that the results from table 5 reflect reliable information.

5.4.3.2 OLS robustness check using BOM data

I repeated the regression analysis from table 6. The BOM_DGD and BOM_AA columns in table 8 reports an excerpt of the results (complete results are attached in the appendix), in comparison to the regression analysis results of BOM_GD and BOM_AD from table 6.

According to the blue part in table 8, there is a difference between the BOM_GD (-0.041) and BOM_DGD (-1.243) for ROA. Due to the insignificance of both results, I accept and believe that the results are quite similar and therefore consistent and robust. The other three robustness check of results show minor differences and are all insignificantly related to the financial firm performance measures ROA and LnTQ. Therefore, I accept and believe that the results are consistent, robust and reflect reliable information.

5.4.4 Conclusion regression analyses

In general the regression analyses, using BOD and BOM data, show similar results. In contrast with my expectations, the size of a firm does not have any influence on financial firm performance. According to Marinova et al. (2010) and Adams and Ferreira (2004), larger firms are more in the public eye and in some cases have to act as role models. In addition, these firms are under more societal pressure for board diversity. Apparently, these arguments do not find any support in this study.

Looking at board size, this control variable is only in one analysis significantly related to financial firm performance. In table 5 column 2, board size is positive and significantly related to LnTQ, using BOD data. This result suggests that, in order to increase LnTQ, the board size of all directors (BOD) have to be increased. One of the arguments which ratify this result is from theory of Van den Berghe & Levrau (2004), who argues that increasing the boardroom provides an increased pool of expertise, and eventually firm performance.

One of the remarkable results, in both table 5 and 6, is the insignificance of the prior performance variables PP_ROA and PP_LnTQ. As explained in chapter 4.2.3.6 I expected that the influence of board diversity on financial firm performance occurs over time (Carter et al., 2010). Obviously, prior performance is insignificantly related to current performance, suggesting that excellent performance is no guarantee for excellent performance for the upcoming year.

After controlling for robustness of the results in the main regression analyses, it turned out that the results of GD and AD are robust and consistent with DGD and AA in both BOD and BOM data sets. The results are qualitatively unchanged. As a conclusion, the OLS regression analyses provide evidence that, for this sample of 95 Dutch listed firms, board diversity is insignificantly related to the performance measures ROA and Tobin’s Q. This implies that both hypothesis 1 and 2, stating that gender and age diversity are positively related to financial firm performance, are not supported in this sample.