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Gender Board Diversity and its Influence on Companies Profit:
A Case of Slovak Top 100 Earning Companies
Ludmila MITKOVA
Faculty of Management, Comenius University in Bratislava, Odbojarov,
Bratislava 25, Slovakia
Email: ludmila.mitkova@fm.uniba.sk
Peter PSENAK
Faculty of Management, Comenius University in Bratislava, Odbojarov,
Bratislava 25, Slovakia
Email: peter.psenak@fm.uniba.sk
Abstract
Our paper examines the gender board diversity and its possible effect on the company’s financial
performance. Data sample consisted of 100 Slovak nonfinancial companies from the Trent Top
ranking with the highest financial performance (profit) in 2018 and 2017. For the selected company’s
managing body information, we used the public access to Slovak Business Register to find out the
gender of the representatives. We used descriptive statistics on the overview of the data sample. Then
we used Plots analyses, Linear regression and Logistical regression to find out the possible statistical
link between the managing body gender diversity and company’s profit. The results of the analysis
confirm the strong underrepresentation of women in the managing bodies in the selected companies.
For the year 2017 there were only 15% of the managing body representatives’ women, and in 2018
16%. Our hypothesis “Higher total number of women in management has a positive effect on
company’s profit” can’t be confirmed according to the statistical findings from the data sample. The
results are limited to the number of selected companies and the underrepresentation of women in the
sample.
Keywords
: gender, managing body, women, diversity, financial performance.
Introduction
In recent years, there have been several studies that have investigated whether gender diversity on
managing board leads to better financial performance or not. First, it should be mentioned that this
issue is strongly connected to the “glass ceiling” phenomenon. In 2016, women were under-
represented at senior management level worldwide (Raji & Alamrani, 2018). Some authors discuss
the so called “queen bee phenomenon” as one of the possible causes of under-representation (Arvate,
Galilea, & Todescat, 2018; Derks, Van Laar, & Ellemers, 2016; Harvey, 2018). Therefore, we can
assume that an overall lower representation of women in decision making or managing bodies is still
a problem and it has to change. This can be done by the top-bottom principle for instance via quotas
set by the government or bottom-up from the companies by them self’s driven by their social
responsibility or other internal directives created by the company. The second way refers to the issue
that gender board diversity is linked to the topic of Corporate social responsibility (Kirchmayer,
Remišová, & Lašáková, 2016; Rentková & Vartiak, 2017; Stachová, Kottulová, & Paškrtová, 2019).
According to a meta-analyses by Post & Byron (2015) female board representation is positively related
to the accounting returns, but not to market performance at all. Greater gender board diversity leads
to lower volatility and better performance in the sample of US based companies (Bernile, Bhagwat, &
Yonker, 2018)
.
Another study from the US by Conyon & He (2017) suggests that the presence of
women on company’s board positively effects the organizational performance and also specially
female directors are crucial for the high performance companies. McKinsey & Co. (2017) are
addressing the issue of women in management and their contribution to the firms performance since
2007 in their reports Women Matter. This means not only on the site of company turnover but also on
the leadership styles. Firm performance and female board directorship was analyzed on French and
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data are showing that the return of assets and return of equity do increase under female directorship
but measured market-based performance decreases (Bennouri, Chtioui, Nagati, & Nekhili, 2018).
German data on critical mass of women on the boards and for the data the “magical number” as
referred by the authors when at least 3 women on boards as for gender diversity (Joecks, Pull, & Vetter,
2013). Data for Czech and Slovak republic are showing a underrepresentation of women in business
sector (Křečková, 2013; Pawera & Štefancová, 2014). Egerová & Nosková (2018) conducted data
from Czech republic and they found out that gender diverse top management teams can be associated
with higher financial performance of the company. In case of Slovakia there is an underrepresentation
of women on the boards of the top financial performing companies (Kottulová & Mitková, 2016;
Mitkova & Kottulova, 2017). This is also a case for the entrepreneurs and SME in Slovakia (Pilková,
Holienka, Kovačičová, Rehák, & Mikuš, 2019; Pilková et al., 2017; Stachová & Musilová, 2019).
Often a simply motivation and intercultural competencies can make a difference in top management
(Kajanová, 2008; Milošovičová, 2019). Companies with gender diverse boards show a positive impact
on market and company performance, and less chance financial misconduct and tend to be les corrupt
(Alliance for Board Diversity & Deloitte, 2019; Lee, Marshall, Rallis, & Moscardi, 2015; Wahid,
2019). On the other hand regional differences in Slovakia can also matter in the case of the company’s
headquarter (Rentková, 2018).
Methodology
Research data sample consists of 100 top Slovak nonfinancial companies based on their financial
performance (in the paper referenced simply as profit). We gathered data from the Trend Top ranking
(Trend Analyses, 2019) and we used the Slovak Business Register (MSSR, 2019) to associate the data
with the gender structure of the managing bodies. We analyzed data from two recent years 2017 and
2018. Assuming, that the number of women in managing bodies (as supervisory board, managing
board, representative) in companies has a positive effect on the yearly profit. We used descriptive
statistics and two different types of regression analysis for potential dependence of the variables. First
of them was a linear regression model, based on only two factors specifically the profit and the number
of women in managing bodies. The second model we used was the logistic regression.
Hypothesis: Higher total number of women in management has a positive effect on company’s profit.
Results and Discussion
The study focuses on the examination of financial performance of a company and the link to the
gender diverse managing body and its effect on the overall profit of the company.
In Table 1 we see that in our data sample we had companies with negative profit, and some with
extremely high as well. The Trend Top 100 lists is based on the company sales therefore it is acceptable
to find companies between them, which do not generate any positive profit yearly. There is a high
difference between the mean and the median which implies the skewness of data in the positive
direction. We can see that the maximum amount of men in management bodies is 25, the mean and
median however implies that that at most between 4 to 5 men are in management body. There is no
company without any men representative. On the other hand, in half of the companies there were no
women representatives in managing bodies.
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Table 1: Summary data on profit and the number of women and men in
managing bodies in 2017
Min.
1st Qu.
Median
Mean
3rd Qu.
Max.
Profit (in thousand)
-27227 3246 8838 35480 29174 449921
Managing bodies – men
Total 503
1 2 4 5.24 7 25
Managing bodies – women
Total 74
0 0 0 0.77 1 5
Source: own calculation based on Trend Top Data (Trend Analyses, 2019)
In case of women the statistics are not that positive. At most 5 women were in managing body and
both the minimum, the second quartile and the median is showing, that mostly no women are in the
management bodies of the selected companies. In 2017 there were altogether 74 women in the
company managing bodies compared to 503 man.
From only the descriptive statistics we can see that there is a major difference between companies in
all three aspects relevant for our research. First, in 2017 there were maximum 5 women board members
in companies against of 25 men, which is an extraordinary difference. Making conclusions from one
year only would be non-dynamic and might be biased in some way, therefore we will need to compare
the results with the year 2018. The profit of the companies has changed dramatically in the year of
2018. The maximum profit fell quite a lot however the losses are lower as well. The skewness of data
is still prevalent, however the differences between companies have been loosened. The mean fell by
at least 5000 Euros but the median only about 90. This shows a fall in outputs overall (Table 2).
Table 2 : Summary data on profit and the number of women and men in
managing bodies in 2018
Min. 1st Qu. Median Mean 3rd Qu. Max.
Profit (in thousand)
-18785 2424 8712 29010 22548 288753
Managing bodies – men
Total 508
1 2 4 5.292 7 21
Managing bodies – women
Total 80
0 0 0.5 0.84 1 6
Source: own calculation based on Trend Top Data (Trend Analyses, 2019)
The first difference seen is that there are no companies in the TOP 100 which would not feature at
least one man. On the other hand, the maximum amount of men has lessened by 4. The median is the
same, but the mean has changed minorly. It got higher, which means that there are more outliers in
the data after the third quartile. Women in managing bodies are still underrepresented in comparison
to men. The minimal value and the 1st quartile didn’t change. But we can see a positive trend
everywhere else. The median went from 0 to 0.5, the mean value had grown as well and the biggest
positive is the value of maximum growing from 5 to 6. We can see that only in one year the number
of women in management positions have grown which is showing a positive trend for them to gain
managerial positions. Now we need to check the total amount. The total number of women and men
in managing bodies have changed year-on-year. Both grew. In case of men by 5 persons and in case
of women by 6. In one year, it might be considered a minimal change, however it is still not redundant.
If the trend will continue at least in this case, we will see more women in managing bodies every year.
In the next step we analyzed the data sample with the help of Plots analyses (see Picture 1).
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Picture 1: Plots analyses for women and profit in 2017 and 2018
Source: own calculation
On the plots above we can see the high spread of the data in both years. They show only a minor
correlation. This however can be revised with other models. As first, we do not differ between test and
train data, because we want to see if there is any potential causality between our data. Firstly, we
created a simple linear regression for the year 2018 (Table 3).
Table 3 : Linear regression for the year 2018
Residuals:
Min
1Q
Median
3Q
Max
-46990 -26498 -19608 -5994 258616
The residuals are non-centered, which is a negative sign. for the overall model performance
Coefficients: Name Estimat
e
Std. Error t value Pr(>|t|)
(Intercept) 28205.0 6999.6 4.030 0.000113 ***
Women in Managing bodies (2018) 965.9 5140.4 0.188 0.851358
Sig. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Source: own calculation
As we expected after the Plots analysis, the independent variable number of women in managing
bodies for the year of 2018 have no relevant effect on the profit of a company. Even in case where no
other values are provided in the model.
Residual standard error:
54240 on 94 degrees of freedom
Multiple R-squared: 0.0003755
Adjusted R-squared: -0.01026
F-statistic: 0.03531 on 1 and 94 DF
p-value: 0.8514
The model evaluation statistics show that a huge amount of residual error remained unexplained which
is shown in F statistic as well. The whole p value of the model is bigger than 0.05 which means that
this model is not optimal for use for future value forecasting it is a static model.
In the next step we decided to choose a dynamic forecasting model. Here we will take the difference
between two years as in the amount of profit between the companies as in the number of women as
well. The difference is created between the years of 2018 and 2017. The outputs in the Table 4 below.
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Table 4: Linear regression for the year 2017
Residuals:
Min
1Q
Median
3Q
Max
-
315288
2101 6447 8811 46541
The residuals are non-centered, which is a negative sign. for the overall model performance
Coefficients: Name Estimat
e
Std. Error t value Pr(>|t|)
(Intercept) -6324 3988 -1.586 0.116
Women in Managing bodies (2017) -8278 18772 -0.441 0.660
Sig. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Source: own calculation
The coefficient of women in managing bodies even through is lower than before is still a value without
effect on the overall model. Therefore, even the midyear changes of profit and number of women in
managing bodies has shown no change in the profit of organizations.
Residual standard error:
38590 on 93 degrees of freedom
Multiple R-squared: 0.002087
Adjusted R-squared: -0.008643
F-statistic: 0.1945 on 1 and 93 DF
p-value: 0.6602
The model statistics are better than in the static 2018-year model, however the whole model on its own
is still not eligible for future data modeling.
The models show that our hypothesis can’t be proven and that the higher number of women in
management have no statistically significant effect on the profit of organization analyzed in our
dataset.
We decided to test another variable: the companies positive profit companies and negative profit
companies. We tried to use the value of women in managing bodies as a decisive value between them.
With this feature engineering, we might be able to see a difference. After adding a new column in our
data, we use logistical regression to classify the companies between positive profit and negative profit
based on the number of women in managing bodies (Table 5).
Table 5: Logistical regression
Deviance Residuals: Min 1Q Median 3Q Max
-
0.94910
0.05090 0.05090 0.08982 0.20659
The residuals are non-centered, which is a negative sign. for the overall model performance
Coefficients: Name Estimat
e
Std. Error t value Pr(>|t|)
(Intercept) 0.94910 0.03563 26.639 <2e-16 ***
Women in Managing bodies -
0.03892
0.02617 -1.488 0.14
Sig. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Source: own calculation
Even in the case of future engineering we were unable to classify the companies between positive and
negative profit ones based only on the number of women managing bodies. This is a prove that our
hypothesis can’t be proven for our dataset and the number of women in managing bodies have not an
impact on the company’s profit in our sample of Trend Top 100 Slovak companies.
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Limited number of the companies and the gender managing body structure of the selected companies
can be considered as a limitation for the provided findings. Therefore, future research of this particular
topic is needed.
Conclusion
The objective of this paper was to investigate the link between the financial performance of the top
performing companies in Slovakia and the diversity in managing bodies. Our hypothesis that diverse
managing body of a company can influence the company’s profit can not been confirmed for our data
sample. One of the major factors of this finding could simply be the fact that in half of the selected
companies there were no women as representatives of managing bodies.
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