Impact of Gender Diversity on ASX Company Board Performance

This report investigates the perceived value of female participation on company boards by analyzing the relationship between gender diversity and financial performance metrics. The study focuses on companies listed on the ASX, examining data from three industry sectors: Consumer Staples, Energy, and Health Care. The research employs statistical analyses, including chi-square and ANOVA tests, to determine if board representation based on gender is dependent on industry sectors and if there are any differences in stock market returns among the industry sectors. Furthermore, the study assesses whether companies with gender-diverse boards outperform those with non-gender-diverse boards in terms of stock market returns and return on assets (ROA). The findings reveal that the industry sectors exhibit varying stock market returns and that company performance, measured by both stock returns and ROA, does not significantly differ between companies with gender-diverse boards and those without. The study also concludes that board representation is not dependent on industry sectors, suggesting that female participation on company boards is perceived as not being valuable, based on the data analyzed.

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Board Diversity
Student’s name:
Course title:
Date:

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Introduction
The following study aims to determine whether female participation in company boards is
perceived to be valuable. The measures of company profitability chosen for the investigation are
return on asset (ROA) and annual company stock market return. Companies were chosen from
the top 500 companies listed on the ASX as at the end of the financial year 2018. The companies
identified were 100 companies in 3 industry sectors (Consumer Staples, Energy and Health
Care). The board of directors with women representation is referred to as a gender-diversity
board. Oppositely, the board of directors with no women representation is referred to as a non-
gender diversity board.
Statistical Analyses
1. Is the representation of female directors on the board dependent on the type of industry
sectors?
To answer this question a chi-square test was chosen. The chi-square test is the most appropriate
since it determines the relationship between two categorical variables1. The categorical variables,
in this case, are board diversity by gender and industry. Consequently, the data of these two
categories are qualitative in nature.
The developed hypothesis is:
H0: There is no difference between gender board diversity and industry
H1: There is a difference between gender-based board diversity and industry
Table 1: Observed
1 Test, Omnibus. "Your chi-square test is statistically significant±now what." Pract Assess Res Eval 20, no. 8
(2015): 2-10.

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Observe
d 1 2 3 Total
Yes 33 31 30 94
No 67 69 70 206
Total 100 100 100 300
Table 2: Expected
Expecte
d 1 2 3 Total
Yes 31 31 31 94
No 69 69 69 206
Total 100 100 100 300
Chi-square = =(((33-31)^2)/31)+(((31-31)^2)/31)+(((30-31)^2)/31)+(((67-69)^2)/69)+(((69-
69)^2)/69)+(((70-69)^2)/69) = 0.233754
Critical Chi-square = χ2 (0.05,2) = 5.991465
Since 0.23 < 5.99, there is sufficient evidence to infer that board representation based on gender
is not dependent on industry sectors.
2. Is there any difference in stock market returns among the 3 industry sectors, that is, Consumer
Staples, Energy and Health Care?
To answer this question, an ANOVA test was chosen. ANOVA was the most appropriate since it
determines whether there are any statistically significant differences between two or more
unrelated groups’ means2.
The three groups chosen, consumer staples, energy, and healthcare were independent and
normally distributed as seen in the figures below.
2 Cuevas, Antonio, Manuel Febrero, and Ricardo Fraiman. "An anova test for functional data." Computational
statistics & data analysis 47, no. 1 (2004): 111-122.

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Consumer Staples
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Energy
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Health Care
Figure 1: Stock market returns histograms based on industry
To determine whether there is any difference in stock market returns among the 3 industry
sectors, the following hypothesis was derived:
H0: μ1 = μ2 = μ3
H1: μ1 ≠ μ2 ≠ μ3
Table 2: Descriptive Statistics
Consumer Staples Energy Healthcare
Sample size 100 100 100
Sample mean 9.86 16.04 14.96
Grand mean 13.62
SST 2180.751
Sample
variance 1414.254 1079.412 755.923
SSE 321709.276
Table 2 shows the descriptive statistics of the three industry where the energy industry had the
highest mean in terms of stock market return of 16.04% followed by the healthcare at 14.96%

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and finally consumer staples at 9.86%. The grand mean of the three industries is 13.62%. From
these, the derived SST was 2180.751. On the other hand, the sample variance of consumer
staples, energy and healthcare was 1414.254, 1079.412 and 755.923 respectively. The resultant
SSE was 321709.276.
From these, the MST and the MSE were derived. The solution is as shown:
MST = SST/ (number of treatments - 1)
= 2180.751 / (3-1) = 1090.375
MSE = SSE/ number of sample data – number of treatments)
= 321709.276 – (300 – 3) = 1083.196
The derived F-stat was:
F-stat = MST/MSE = 1090.375/1083.196 = 1.007
From the F-tables, F (0.05, 2, 297) = 3.026.
Rejection region > 3.026.
Since 1.007 < F critical, we choose to reject the null hypothesis. Thus, there is a difference
between stock market returns among the 3 industry sectors
3. Do companies with non-gender diversity boards underperform (i.e. have lower stock returns),
compared to companies with gender diversity boards in stock market returns?
Similarly, an ANOVA test was chosen. The two groups chosen were independent and normally
distributed as seen in the figures below.

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No Board Diveristy
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Diverisfied Board
To determine whether there is any difference in company performance (in terms of stock returns)
between the diversity board the following hypothesis was derived:
H0: μ1 = μ2
H1: μ1 ≠ μ2
Table 3: Descriptive Statistics
Non- gender diversity board Gender-diversity board
Sample size 206 94
Sample mean 4.93 32.67
Grand mean 13.62
SST 49695.439
Sample variance 915.904 929.401
SSE 274194.588
From table 3 above, it is evident that companies with gender diversity on the board have a higher
performance (32.67%) compared to those with none (4.93%) with the fact that most of the
companies (206) had no gender diversity in their boards. With a grand mean of 13.62 among the
industries, the SST was 49695.439 and the SSE was 274194.588 given the variances were
915.904 and 929.401 for companies with no board diversity and companies with board diversity
respectively.

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From these, the MST and the MSE were derived. The solution is as shown:
MST = SST/ (number of treatments - 1)
= 24847.72 / (2-1) = 49695.439
MSE = SSE/ (number of sample data – number of treatments)
= 274194.588 – (300 – 2) = 920.116
The derived F-stat was:
F-stat = MST/MSE = 49695.439 / 920.116 = 54.01
From the F-tables, F (0.05, 1, 298) = 3.873.
Rejection region > 3.873.
Since 54.01 > F critical, we choose to not reject the null hypothesis. Thus, there is no difference
in company performance (in terms of stock returns) between the diversity boards based on
gender.
4. Do companies with gender diversity boards outperform (i.e. have higher ROA), compared to
companies with non-gender diversity boards in ROA?
An ANOVA test was also chosen to answer this question. The two groups chosen were
independent and normally distributed as seen in the figures below.

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0 1.3 2.6 3.9 5.2 6.5 7.8 9.1 10.4 11.7 13
Frequency
0 11.5 23 34.5 46 57.5 69 80.5 92 103.5 115 126.5
Frequency
To determine whether there is any difference in company performance (in terms of ROA)
between the diversity board the following hypothesis was derived:
H0: μ1 = μ2
H1: μ1 ≠ μ2
Table 4: Descriptive statistics
Non- gender-diversity board Gender-diversity board
Sample size 206 94
Sample mean 4.88 17.45
Grand mean 8.82
SST 10205.987
Sample variance 7.254 280.440
SSE 27567.986
Evidently, companies with gender diversity on the board have a higher performance (17.45%)
compared to those with none (4.88%) with the fact that most of the companies (206) had no
gender diversity in their boards. With a grand mean of 17.45% among the industries, the SST
was 10205.987 and the SSE was 27567.986 given the variances were 7.254 and 280.440 for
companies with no board diversity and companies with board diversity respectively.
From these, the MST and the MSE were derived. The solution is as shown:
MST = SST/ (number of treatments - 1)

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= 10205.987/ (2-1) = 10205.987
MSE = SSE/ (number of sample data – number of treatments)
= 27567.986 – (300 – 2) = 92.51
The derived F-stat was:
F-stat = MST/MSE = 10205.987 / 92.51 = 110.323
From the F-tables, F (0.05, 1, 298) = 3.873.
Rejection region > 3.873.
Since 110.323 > F critical, we choose to not reject the null hypothesis. Thus, there is no
difference in company performance (in terms of ROA) between the diversity boards based on
gender.
Conclusion
The industries were found to have varying stock market returns where the energy industry had
the highest average returns. The fact has been proved by the data analysis where it was seen that
the company performance (in both stock returns and ROA terms) were found out to have no
difference between companies with gender diversity boards and companies with non-gender
diversity boards. On the other hand, it was also established that there was sufficient evidence that
board representation is not based on industry sectors. It is evident that female participation in a
company’s board is perceived as not being valuable.

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List of Bibliography
Cuevas, Antonio, Manuel Febrero, and Ricardo Fraiman. "An anova test for functional
data." Computational statistics & data analysis 47, no. 1 (2004): 111-122.
Test, Omnibus. "Your chi-square test is statistically significant±now what." Pract Assess Res
Eval 20, no. 8 (2015): 2-10.