CONCLUSIONS
This study examines the impact of BOD characteristics on the financial leverage
ratio of nonfinancial companies listed on the Ho Chi Minh City Stock Exchange for eight
years from 2012 to 2019. The dataset includes 199 companies with 1,592 observations.
The system GMM used to analyze the data helps avoid correlation between the
instrumental variables and the errors, overcomes the endogeneity problem, and estimates
the regression coefficients consistently.
The research results show that, in general, the characteristics of the BOD have an
impact on a firm’s financial leverage decisions, with two out of four variables reflecting
the characteristics of the BOD being statistically significant. Specifically, the number of
meetings of the BOD during the year has a negative and statistically significant
coefficient. This shows that companies with more active BODs, as indicated by the
number of meetings per year, often control their leverage at a lower level than firms with
less active boards. In addition, board gender diversity, whether defined by the number of
female directors, the proportion of female directors, or the presence of female directors,
is negatively correlated with financial leverage and remains statistically significant at 5%
in all three specifications. This can be explained by a higher level of risk aversion among
women, which influences board decisions regarding the level of borrowing, resulting in
lower debt ratios compared to boards without female directors. The size of the BOD and
CEOs with dual roles were found to have no impact on the leverage ratio.
This result complements and follows previous studies on the influence of
governance structure on corporate capital structure, which is the basis for providing
suggestions to managers on operating and managing their businesses.

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ed studies focus on finding factors that influence the
capital structure of firms listed on the Vietnamese stock markets. Specifically, Đặng and
Quách (2014) identify three factors that have a strong impact on the capital structure of a
firm, namely, firm size, profitability (positive impact), and taxes (negative impact).
Previously, Trương and Võ (2008) affirm that capital structure is positively correlated
with company size, industry, and revenue growth and is inversely correlated with
profitability. In addition, capital structure is positively correlated with the number of
directors. Recently, a series of studies on factors affecting the capital structure of firms in
specific industries was also conducted. The studies included firms in the logistics industry
(Lương, Phạm, Nguyễn, Nguyễn, Nguyễn, & Phạm, 2020), the food industry (Lê, Bùi, &
Lê, 2020), the Vietnam Oil and Gas Group (Vũ & Nguyễn, 2013), and the seafood
industry (Nguyễn, 2008). Most studies show that firm size, growth rate, and profitability
are positively correlated with capital structure. Some other factors that are negatively
correlated are also mentioned, including taxes, liquidity, profits, and business risk.
Most of the research on Vietnam markets mentioned above use relatively small
datasets over short time periods and focus on factors affecting capital structure. Only in
the last few years have some specific studies on the relationship between corporate
governance and capital structure been published. Nguyễn, Trần, Nguyễn, Võ, and Nguyễn
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(2016) examine the impact of corporate governance (state ownership, financial
institutions, foreign investors, members of the BOD, and the largest shareholders of the
firm) and firm characteristics (size, profitability, tangible assets, tax shield, and the gap
between optimal leverage and observed leverage) on capital structure decisions. Research
results show that corporate capital structure not only depends on the characteristics of the
business, but is also influenced by enterprise ownership characteristics. In another study
by Phan, Trần, and Trần (2017), the role of CEO duality is examined. Their study
confirms that firms with a dual leadership structure performed more effectively.
As previous research is still inconclusive, a comprehensive study with a large set
of data on companies listed on the Vietnamese stock market would provide significant
insights into the relationship between BOD structure and capital structure (financial
leverage).
Based on the theory and previous research results, this study hypothesizes that:
• H1: Board size has an impact on financial leverage.
• H2: The number of annual board meetings has an impact on financial
leverage.
• H3: The number of female directors on the BOD has an impact on financial
leverage.
• H4: CEO duality has an influence on financial leverage.
3. DATA AND RESEARCH METHOD
3.1. Definitions of variables and data collection method
This study examines the relationship between the BOD structure and the financial
leverage of companies listed on the Vietnamese stock market. For quantitative analysis,
the authors use leverage ratio, which is defined as the ratio of total debt to total assets of
the firm, similar to the study by Haque, Arun, and Kirkpatrick (2011).
The independent variables used in this study include the size of the board, the
number of board meetings per year, the number of female directors on the board, and an
indicator variable indicating whether the chairman also holds a CEO position. To increase
the effectiveness of the estimate, two variables representing board size and the number of
board meetings were converted to logarithms prior to analysis. In addition to the number
of female directors on the board, the authors also use two other definitions of gender
diversity of the BOD, namely, the percentage of female directors on the board and an
indicator variable indicating the presence of female directors. Using different definitions
of the gender variable in the analysis will help increase the reliability of the results.
To control the impact of other factors on a firm's leverage, the authors use firm
size, fixed assets, liquidity, profitability, and business growth rate as control variables,
Hoang Mai Phuong and Nguyen Thanh Hong An
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similar to studies on the effect of firm characteristics on financial leverage according to
Bradley, Jarrell, and Kim (1984), Castanias (1983), Long and Malitz (1985), and Titman
and Wessels (1988). These studies generally agree that financial leverage has a positive
relationship with firm size, fixed assets, and growth rate, and an inverse relationship with
returns and liquidity.
Detailed definitions of the variables are presented in Table 1.
Table 1. Variable definitions
Variable Code Definition/Formula
Dependent variable
Financial leverage Lev Total debt/Total assets
Independent variables
BOD size Lbsize Logarithm (Number of directors on the board)
BOD meeting frequency Lmeet Logarithm (Number of BOD meetings per year)
Female directors Female Number of female directors on the BOD
CEO duality Ceodual Equals 1 if the CEO is also the BOD chairman, 0 otherwise
Control variables
Firm size Lfsize Logarithm (Total assets)
Fixed assets Fixed_assets (Total assets - Short-term assets)/Total assets
Liquidity Liquidity Short-term assets/Short-term liabilities
Profitability ROA Net profit/Total assets
Growth Salegrowth Annual sales growth
The data are collected from audited financial reports, annual reports, and annual
executive reports of nonfinancial companies listed on the Ho Chi Minh City Stock
Exchange from 2012 to 2019. Companies with insufficient data are excluded from the
sample.
3.2. Research method
To analyze the relationship between the variables representing the characteristics
of the BOD and the leverage ratio, the authors propose the following research Model:
𝐿𝑒𝑣𝑖𝑡 = 𝛽1 + 𝛽2𝐿𝑏𝑠𝑖𝑧𝑒𝑖𝑡 + 𝛽3𝐿𝑚𝑒𝑒𝑡𝑖𝑡 + 𝛽4𝐹𝑒𝑚𝑎𝑙𝑒𝑖𝑡 + 𝛽5𝐶𝑒𝑜𝑑𝑢𝑎𝑙𝑖𝑡
+ 𝛽6𝐿𝑓𝑠𝑖𝑧𝑒𝑖𝑡 + 𝛽7𝐹𝑖𝑥𝑒𝑑_𝑎𝑠𝑠𝑒𝑡𝑠𝑖𝑡 + 𝛽8𝑅𝑂𝐴𝑖𝑡 + 𝛽9𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦𝑖𝑡
+ 𝛽10𝑆𝑎𝑙𝑒𝑔𝑟𝑜𝑤𝑡ℎ𝑖𝑡 + 𝜀𝑖𝑡
(1)
To estimate Model (1), the authors use the ordinary least squares method with
panel data (POLS). However, estimation by this method does not guarantee consistency
for two reasons. First, the omitted factors are likely to interact with the independent
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variables in the Model, causing the estimate to be biased and inconsistent. Second, since
this Model does not take advantage of information from the differences between firms,
the estimates may be less accurate (Wooldridge, 2002).
As a remedy, the authors restructure Model (1) to incorporate the differences
among companies (representing by in the new Model) in the dataset:
𝐿𝑒𝑣𝑖𝑡 = 𝛽1 + 𝛽2𝐿𝑏𝑠𝑖𝑧𝑒𝑖𝑡 + 𝛽3𝐿𝑚𝑒𝑒𝑡𝑖𝑡 + 𝛽4𝐹𝑒𝑚𝑎𝑙𝑒𝑖𝑡 + 𝛽5𝐶𝑒𝑜𝑑𝑢𝑎𝑙𝑖𝑡
+ 𝛽6𝐿𝑓𝑠𝑖𝑧𝑒𝑖𝑡 + 𝛽7𝐹𝑖𝑥𝑒𝑑_𝑎𝑠𝑠𝑒𝑡𝑠𝑖𝑡 + 𝛽8𝑅𝑂𝐴𝑖𝑡 + 𝛽9𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦𝑖𝑡
+ 𝛽10𝑆𝑎𝑙𝑒𝑔𝑟𝑜𝑤𝑡ℎ𝑖𝑡 + 𝜇𝑖 + 𝜀𝑖𝑡
(2)
Model (2) is estimated by the regression method with random and fixed effects,
respectively. The LM test is used to choose between the POLS regression Model and the
regression Model with random effects. Then, the Hausman test is used to choose between
the regression Model with random effects and the regression Model with fixed effects.
However, the recent research of Liao, Mukherjee, and Wang (2015) indicates that
firms tend to adjust their leverage toward an optimal value over time. As discussed, if the
BOD actually impacts financial leverage decisions, the adjustment effect implies that the
BOD would refer to the past leverage level when deciding the future leverage ratio. In
other words, Levit and Levit-1 are correlated. The fact that Model (2) omits this important
variable (i.e., Levit-1) reduces the accuracy of the estimates. Furthermore, if Levit-1 is
correlated with the present structure and operation of the BOD, a case which is raised in
previous research by Berger et al. (1997), the omission of Levit in Model (2) would render
the estimates inefficient and inconsistent. As a remedy, Model (2) is restructured as follows:
𝐿𝑒𝑣𝑖𝑡 = 𝛽1 + 𝛽2𝐿. 𝐿𝑒𝑣𝑖𝑡 + 𝛽3𝐿𝑏𝑠𝑖𝑧𝑒𝑖𝑡 + 𝛽4𝐿𝑚𝑒𝑒𝑡𝑖𝑡 + 𝛽5𝐹𝑒𝑚𝑎𝑙𝑒𝑖𝑡
+ 𝛽6𝐶𝑒𝑜𝑑𝑢𝑎𝑙𝑖𝑡 + 𝛽7𝐿𝑓𝑠𝑖𝑧𝑒𝑖𝑡 + 𝛽8𝐹𝑖𝑥𝑒𝑑_𝑎𝑠𝑠𝑒𝑡𝑠𝑖𝑡 + 𝛽9𝑅𝑂𝐴𝑖𝑡
+ 𝛽10𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦𝑖𝑡 + 𝛽11𝑆𝑎𝑙𝑒𝑔𝑟𝑜𝑤𝑡ℎ𝑖𝑡 + 𝜇𝑖 + 𝜀𝑖𝑡
(3)
Model (3) cannot be consistently estimated by the methods used for Models (1)
and (2) because the endogeneity problem caused by the inclusion of the variable Levit-1.
Instead, Model (3) is estimated using the system GMM method by Blundell and Bond
(1998). This method provides a plausible solution for endogeneity when valid instruments
are not available, which is a common problem encountered by researchers in the field of
corporate governance (Nguyen, Locke, & Reddy, 2015). In particular, this method utilizes
a set of instrument variables derived from the available dataset, namely, lagged and
differenced variables of the endogenous variables. Together with the estimation method
using moments, the system GMM produces consistent estimates.
Hoang Mai Phuong and Nguyen Thanh Hong An
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4. RESULTS AND DISCUSSION
4.1. Descriptive statistics
The authors collected data based on the variable definitions and methodology
presented in Section 3. The final dataset included 1,592 observations collected from 199
firms from 2012 to 2019. Summary statistics are presented in Table 2.
Table 2. Descriptive statistics
Number of
observations Mean Median Std. dev. Min Max
Lev 1,592 0.49 0.50 0.21 0.03 1.29
Bsize 1,592 5.76 5.00 1.33 1.00 11.00
Meet 1,592 10.97 7.00 11.61 1.00 170.00
Female 1,592 0.90 1.00 1.04 0.00 6.00
Female_% 1,592 0.15 0.14 0.17 0.00 1.00
Female_dummy 1,592 0.55 1.00 0.50 0.00 1.00
Ceodual 1,592 0.28 0.00 0.45 0.00 1.00
Lfsize 1,592 21.17 21.08 1.26 18.46 26.72
Fixed_assets 1,592 0.41 0.37 0.23 0.01 0.98
ROA 1,592 0.06 0.05 0.08 -0.85 0.78
Liquidity 1,592 2.28 1.58 2.46 0.05 39.37
Salegrowth 1,592 0.18 0.07 1.53 -24.16 29.56
Notes: The analysis is performed based on 1,592 observations collected from 199 nonfinancial companies
listed on the Ho Chi Minh City Stock Exchange from 2012 to 2019; Bsize is the number of board members;
Lev is the ratio of total debt to total assets of the business; Meet is the number of board meetings per year;
Female is the number of female members on the Board of Directors. Female_% is the percentage of female
directors; Female_dummy is an indicator variable, with the value equal to 1 if the board has female
members and 0 if not; Ceodual is an indicator variable with value equal to 1 if the chairman of the BOD
also holds the position of CEO and 0 if not. Lfsize is the logarithm of the firm's total book value;
Fixed_assets is the ratio of fixed assets to total assets; ROA is the ratio of after-tax profit to total assets;
Liquidity is the ratio of short-term assets to short-term liabilities. Salegrowth is the rate of sales growth.
Based on Tables 2 and 3, we can see that the average leverage of the surveyed
enterprises is about 49.0%. This figure is significantly higher than the figure of 29.0% in
the study of Nguyen, Locke, and Reddy (2015) on the companies listed on the Ho Chi
Minh City stock market from 2008 to 2012. The average leverage ratio of businesses was
kept quite stable over the years surveyed, ranging from 47.7% to 49.6%. However, there
was a clear difference in leverage between different industries. The Arts and
Entertainment sector had the lowest leverage, at around 13.1%, while the Wholesale and
Construction–Real Estate sectors had the highest leverages, exceeding 58.0%. Figure 1
also shows that the leverage ratios of industries also had different fluctuations over the
years. This information not only gives us a general picture of the leverage ratio of the
businesses listed on the Ho Chi Minh City stock exchange but is also very useful in
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estimating the regression in the following section. As for the independent variables, the
surveyed firms' boards of directors have an average of about six members, with the
majority of the firms having a BOD consisting of five members. This number is almost
equivalent to that found by Nguyen et al. (2015) for 2008 to 2011, showing that the size
of the BOD of listed companies in Vietnam remained quite stable through the years.
Boards usually hold an average of 11 meetings per year, but most of them only hold about
seven meetings. Regarding the proportion of women, the BODs of the surveyed
companies average 15% female members (equivalent to about one person) and more than
55% of the companies surveyed have female directors. This figure is higher than in the
study of Nguyen et al. (2015), in which the proportion of women directors was 12% and
about 51% of surveyed firms had female board members. This is a sign that there has
been an improvement in gender diversity on the boards of Vietnamese-listed companies
in recent years. Finally, about 28% of the surveyed businesses have a chairman of the
board who concurrently holds the position of CEO. This figure has decreased by 4%
compared with the corresponding study of Nguyen et al. (2015) for 2008 to 2012.
Table 4 presents the correlation coefficients among the variables. The results show
that the correlation coefficients between the independent and control variables are below
0.5, implying that multicollinearity is not a serious problem in the regression analysis.
This conclusion is supported by the calculated VIF for the independent and control
variables of less than 2. In addition, the correlation analysis results show that the number
of board meetings is positively correlated, while board size is negatively correlated, with
firm leverage. However, this result is not reliable because the correlation analysis does
not take into account the impact of other covariates on the relationship between the two
considered variables. To analyze the relationship between the independent variables and a
firm's leverage ratio consistently and effectively, regression analysis should be performed.
Figure 1. Financial leverage by industries
Notes: Companies are classified by their first registered type of business. Industries are classified in
accordance with NAICS (North American Industry Classification System) 2007.
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Table 3. Financial leverage by various industries
2012 2013 2014 2015 2016 2017 2018 2019 Mean
Wholesale 0.6462 0.6185 0.6081 0.5859 0.5629 0.5573 0.5487 0.5538 0.5852
Retail 0.5491 0.5644 0.5196 0.5360 0.5484 0.5732 0.5040 0.5209 0.5394
Information Technology 0.4706 0.4939 0.5120 0.4918 0.4852 0.4343 0.4619 0.4766 0.4783
Accommodation and
Catering 0.3022 0.2541 0.2810 0.5585 0.5690 0.3811 0.4367 0.5068 0.4112
Mining 0.3388 0.3002 0.2861 0.3183 0.2498 0.2512 0.2940 0.2571 0.2869
Arts and Entertainment 0.1309 0.1498 0.1558 0.1304 0.1313 0.1153 0.1214 0.1107 0.1307
Production 0.4431 0.4583 0.4496 0.4423 0.4502 0.4606 0.4665 0.4582 0.4536
Agriculture 0.4282 0.3625 0.3295 0.3733 0.2975 0.3361 0.3333 0.4119 0.3590
Utilities 0.4936 0.4735 0.4576 0.4550 0.4566 0.4606 0.4430 0.4493 0.4611
Transportation and
Warehousing 0.4058 0.4002 0.3994 0.4076 0.3926 0.3777 0.3668 0.3408 0.3864
Construction and Real
Estate 0.5845 0.5856 0.5855 0.5859 0.5839 0.5862 0.5876 0.5658 0.5831
Mean 0.4953 0.4959 0.4880 0.4874 0.4839 0.4872 0.4859 0.4774 0.4876
Notes: Companies are classified by their first registered type of business. Industries are classified in
accordance with NAICS (North American Industry Classification System) 2007.
Table 4. Correlation coefficients of research variables
Lev Lbsize Lmeet Female Ceodual Lfsize Fixasset ROA Liquidity
Sale-
growth
Lev 1.00
Lbsize -0.05** 1.00
Lmeet 0.12*** 0.09*** 1.00
Female -0.03 0.25*** 0.02 1.00
Ceodual 0.02 -0.01 0.01 0.10*** 1.00
Lfsize 0.28*** 0.29*** 0.20*** 0.17*** -0.01 1.00
Fixasset -0.16*** 0.17*** -0.11*** -0.05* -0.11*** 0.10*** 1.00
ROA -0.46*** 0.10*** -0.04 0.04 -0.06** -0.04* -0.05* 1.00
Liquidity -0.46*** -0.02 -0.03 -0.03 0.07*** -0.14*** -0.15*** 0.29*** 1.00
Salegrowth 0.01 0.02 0.05** 0.04* 0.05* 0.07*** -0.05** -0.01 0.02 1
Notes: *, **, and *** correspond to the 10%, 5%, and 1% levels of significance, respectively. The analysis
is based on 1,592 observations collected from 199 nonfinancial companies listed on the Ho Chi Minh City
Stock Exchange from 2012 to 2019. Bsize is the number of board members. Lev is the ratio of total debt to
total assets of the business. Meet is the number of board meetings per year. Female is the number of female
members of the Board of Directors. Female_% is the percentage of female directors. Female_dummy is an
indicator variable equal to 1 if the board has female members, and 0 if not. Ceodual is an indicator variable
equal to 1 if the chairman of the BOD also holds the position of CEO, and 0 if not. Lfsize is the logarithm
of the firm's total book value. Fixed_assets is the ratio of fixed assets to total assets. ROA is the ratio of
after-tax profit to total assets. Liquidity is the ratio of short-term assets to short-term liabilities. Salegrowth
is the rate of sales growth.
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4.2. Regression analysis
To analyze the relationship between the characteristics of the BOD and financial
leverage ratio, the authors conducted a preliminary empirical analysis using a static Model
structured as in Models (1) and (2) of Section 3. The results are shown in Table 5.
Before interpreting the results, a number of standard tests were performed to select
the optimal Model. First, Breusch-Pagan’s LM test was performed to choose between the
POLS Model (Column 1) and the regression Model with random effects (Column 2). The
results show that the test statistic, Chibar-square (1) = 22.800, corresponds to the value
Prob (chibar-square) = 0.000, indicating that the Model with random effects (Column 2)
was more effective than the POLS Model (Column 1). To choose between the random
effects Model (Column 2) and the fixed effects Model (Column 3), the Hausman test was
performed. The results show that the test statistic Chi-square (16) = 1,055.300, which
corresponds to Prob (chi-square) = 0.000, and that the fixed effects Model (Column 3) is
more consistent and efficient than the random effects Model (Column 2). In conclusion,
the two tests indicate that the Model with fixed effects should be used.
First, the regression results with the POLS Model (Column 1) suggest that the
makeup of the BOD has an impact on the firm's leverage decisions. Specifically, the
variable Female has a negative coefficient and is statistically significant at 1%, implying
that having more female directors is associated with a lower rate of financial leverage.
The remaining three characteristics of the board, namely, size of the board (Lbsize),
number of meetings per year (Lmeet), and a CEO who is also the chairman of the board
(Ceodual), have no impact on a firm’s leverage ratio. However, the results are not reliable
because estimation by the POLS method can be biased and inconsistent due to the
omission of firm-level characteristics.
To take into account unobserved factors at the firm level, the tests show that the
regression Model with fixed effects (Column 3) is optimal. The regression results in
Column (3) show that, after taking into account other unobservable characteristics at the
firm level, the variables represent the performance of the BOD, including the size of the
board (Lbsize), the number of meetings per year (Lmeet), the number of female members
(Female), and whether the CEO is also the chairman of the BOD (Ceodual), have no
impact on the leverage ratio of the firms. In Columns (4) and (5), the authors re-estimate
Model (2) with two other common definitions of board gender diversity, namely, the
proportion of female directors (Female_%) and presence of female directors
(Female_dummy).
In summary, the results of the static Model suggest that the BOD does not have
an impact on the financial leverage ratio of businesses listed on the Ho Chi Minh City
Stock Exchange. This result is contrary to the findings of Nguyen et al. (2015) and Phan
et al. (2017).
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4.3. Robust tests
Some recent studies have shown that firms tend to keep their leverage at a target
level they consider optimal. This means that when the leverage is higher than the target,
the business will adjust it downward and when the leverage is lower than the target, they
will adjust it upward (Liao et al., 2015). This adjustment mechanism implies that leverage
in the present (Levit) is correlated with leverage in the past (L.Levit). To the extent that
this is true, Models (1) and (2) have omitted an important factor (L.Levit), and this would
lead to inefficiencies in the estimates presented in Table 5.
More seriously, if the omitted variable and the independent variables (in this case,
the quality of the board's performance) are correlated (the so-called endogenous
phenomenon), estimates for the relationship between the BOD’s quality and the firm’s
financial leverage would be biased and inconsistent, implying that the conclusions based
on the static Model in Table 5 do not reflect reality. This possibility is even more evident
as some past studies, such as Kyereboah-Coleman and Biekpe (2006), Abor (2007),
Bokpin and Arko (2009), and Rose et al. (2013), have shown that the BOD really has a
voice in deciding a firm’s financial leverage. Correlation analysis between L.Levit and the
independent variables in Model (2) also shows that the correlation coefficient between
L.Levit and Lbsizeit is -0.05 and statistically significant at 10%, the correlation coefficient
between L.Levit and Lmeetit is 0.13 and statistically significant at 1%, the correlation
coefficient between L.Levit and Lfsizeit is 0.25 and statistically significant at 1%, the
correlation coefficient between L.Levit and Fixed_assetsit is -0.16 and statistically
significant at 1%, the correlation coefficient between L.Levit and ROAit is -0.39 and
statistically significant at 1%, and the correlation coefficient between L.Levit and
Liquidityit is -0.42 and statistically significant at 1%. Based on the results of previous
studies and preliminary empirical evidence, we find that endogeneity is very likely to
exist, and the hypothesis testing results based on the estimates in Table 5 may not be
correct. Therefore, the authors reconstructed Model (2) by adding the one-step lag
variable of the dependent variable (L.Levit) to the list of independent variables. The
modified Model is shown in Model (3).
Model (3) was re-estimated using the POLS method (Column (1)) and the
regression method with fixed effects (Column (2)). However, estimates using POLS or
fixed effects regression cannot consistently estimate the regression coefficients in the
presence of endogeneity due to dynamic Model structures (Blundell & Bond, 1998).
Therefore, Model (3) was re-estimated by the system GMM method, which is capable of
estimating the regression coefficients consistently in the presence of endogeneity. The
results are presented in Column (3). Columns (4) and (5) present the estimation results of
Model (3) by the system GMM method with the variable Female replaced by Female_%
and Female_dummy.
Estimated results in all Columns in Table 6 show that L.Levit has a positive and
statistically significant correlation of 1% with Levit. This means that the dynamic Model
is valid. However, with dynamic Model structure, estimates by the POLS and fixed effects
regression methods for Model (3) presented in Columns (1) and (2) are inconsistent.
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Columns (3), (4), and (5) present the estimates for Model (3) by the system GMM method
with the variables Female, Female_%, and Female_dummy, respectively. The Arellano-
Bond test statistic for series correlation shows that the second-order series correlation,
AR (2), does not exist. Therefore, the authors chose lag variables of order 3 onwards as
instrumental variables. This eliminates the correlation between the instrumental variables
and the error and remedies the endogeneity problem completely. The Hansen J-test
statistic in all three Columns is not statistically significant, i.e., the instrumental variables
are not correlated with the error term, suggesting that the endogeneity problem is fixed
(Wintoki, Linck, & Netter, 2012; Roodman, 2009).
The results in Columns (3), (4), and (5) show that, in general, the quality of the
board of directors has an impact on the firm's financial leverage decisions, with two out
of four variables reflecting the characteristics of the board of directors having statistically
significant coefficients. Specifically, the number of board meetings during the year is
negatively correlated and statistically significant in the Model at the 5% and 10% levels
with the variables Female and Female_dummy. This implies that enterprises with a large
number of BOD meetings per year often have low financial leverage. Conversely,
leverage ratios are higher in businesses where BODs are less active. Vafeas (1999)
suggests that the number of board meetings demonstrates a positive effect on the
performance of the board and is a good representation of managerial oversight. Therefore,
the research results show that companies with more-active BODs often control their
leverage at a lower level than firms with less-active boards. The frequency of board
meetings reduces the debt ratio, indicating the effort of the BOD in actively monitoring
financial operations (Anderson et al., 2004). This result is similar to that of Stephanus et
al. (2014) and Vafeas (1999).
The gender diversity of the BOD, whether defined by the number of female
directors, the proportion of female directors, or the presence of female directors, is
negatively correlated and remains statistically significant at the 5% level in all three
specifications. These results are consistent with the studies of Harris (2014) and Abobakr
and Elgiziry (2015). The negative correlation between board gender diversity and a firm’s
capital structure can be explained by the characteristics and role of women directors.
Specifically, female directors are often believed to have a different way of thinking and a
different attitude toward risk than their male counterparts (Adams & Funk, 2012). Female
directors are considered stricter in supervisory work (Abobakr, & Elgiziry, 2015; Adam,
& Ferreira, 2009), have a more conservative attitude in risk assessment (Jiankoplos, &
Bernasek, 1998), and typically have behavior and roles similar to independent directors
(Adams, de Haan, Terjesen, & van Ees, 2015). Based on the characteristics that previous
studies have pointed out, female directors are more likely to be stricter and more cautious
in decisions regarding capital structure than their male counterparts. This may, in part,
explain why firms with female members on the board of directors usually have lower
leverage ratios. Contrary to previous studies, the size of the BOD and CEO duality were
found to have no impact on a firm’s leverage ratio. This result differs from those of Berger
et al. (1997), Anderson et al. (2004), Abor (2007), Bokpin and Arko (2009), Kyereboah-
Coleman and Biekpe (2006), Rose et al. (2013) and Phan et al. (2017).
Hoang Mai Phuong and Nguyen Thanh Hong An
90
Table 5. Regression results for static Models
Variable
POLS_static
(1)
Random_static
(2)
Fixed_static 1
(3)
Fixed_static 2
(4)
Fixed_static 3
(5)
Lbsize
-0.0090
[0.650]
-0.0040
[0.820]
-0.0010
[0.960]
-0.0040
[0.860]
-0.0020
[0.930]
Lmeet
0.0050
[0.420]
-0.0030
[0.530]
-0.0060
[0.400]
-0.0060
[0.410]
-0.0060
[0.400]
Female
-0.0120***
[0.000]
-0.0050
[0.210]
-0.0020
[0.730]
Female_%
-0.0250
[0.530]
Female_dummy
-0.0050
[0.710]
Ceodual
0.0080
[0.370]
0.0000
[0.955]
-0.0020
[0.880]
-0.0020
[0.890]
-0.0020
[0.890]
Lfsize
0.0420***
[0.000]
0.0780***
[0.000]
0.1010***
[0.000]
0.1010***
[0.000]
0.1010***
[0.000]
Fixed_assets
-0.2150***
[0.000]
-0.1300***
[0.000]
-0.1040**
[0.030]
-0.1040**
[0.030]
-0.1040**
[0.030]
ROA
-0.7720***
[0.000]
-0.4970***
[0.000]
-0.4720***
[0.000]
-0.4720***
[0.000]
-0.4720***
[0.000]
Liquidity
-0.0310***
[0.000]
-0.0190
[0.000]
-0.0170***
[0.000]
-0.0170***
[0.000]
-0.0170***
[0.000]
Salegrowth
-0.0030
[0.100]
0.0020*
[0.060]
0.0020**
[0.040]
0.0020**
[0.040]
0.0020**
[0.050]
Constant
-0.1330*
[0.090]
-0.9120***
[0.000]
-1.4910***
[0.000]
-1.4920***
[0.000]
-1.4880***
[0.000]
Year dummies Yes Yes Yes Yes Yes
Industry
dummies
Yes Yes No No No
Number of
observations
1,592 1,592 1,592 1,592 1,592
R2 0.4950 0.3853 0.3330 0.3330 0.3330
F statistic (or
Wald statistic)
49.7260 811.6100 9.2830 9.4630 9.2930
P-value 0.0000 0.0000 0.0000 0.000 0.0000
Notes: *, **, and *** correspond to the 10%, 5%, and 1% levels of significance, respectively.
DALAT UNIVERSITY JOURNAL OF SCIENCE [ECONOMICS AND MANAGEMENT]
91
The analysis is performed on 1,592 observations collected from 199 nonfinancial
companies listed on the Ho Chi Minh City Stock Exchange from 2012 to 2019. Lev is the
dependent variable, defined as the ratio of total debt to total assets of the business. Bsize
is the number of board members. Meet is the number of board meetings per year. Female
is the number of female members of the Board of Directors. Female_% is the percentage
of female directors. Female_dummy is an indicator variable equal to 1 if the board has
female members, and 0 if not. Ceodual is an indicator variable equal to 1 if the chairman
of the BOD also holds the position of CEO, and 0 if not. Lfsize is the logarithm of the
firm's total book value. Fixed_assets is the ratio of fixed assets to total assets. ROA is the
ratio of after-tax profit to total assets. Liquidity is the ratio of short-term assets to short-
term liabilities. Salegrowth is the rate of sales growth. Column (1) presents the regression
results for Model (1), using the POLS regression method. Column (2) presents the
regression results for Model (2) using the regression method with random effects. Column
(3) presents the regression results for Model (2), using the regression method with fixed
effects. Column (4) presents the regression results for Model (2), using the regression
method with fixed effects and variable Female_% replacing variable Female. Column (5)
presents the regression results for Model (2), using the regression method with fixed
effects and variable Female_dummy replacing variable Female. The test statistic of the
LM test for random effects is Chibar-square(1) = 22.8 (Prob(chibar-square)=0.000),
indicating that the Model with random effects (Column 2) is more efficient than the POLS
Model (Column 1). The test statistic for the Hausman test is Chi-square(16)=1055.3
(Prob(chi-square)=0.000), indicating that the Model with fixed effects (Column 3) is
consistent and more efficient than the Model with random effects (Column 2). In general,
the Model used for analysis is the fixed effects Model.
Model (1) is structured as follows:
𝐿𝑒𝑣𝑖𝑡 = 𝛽1 + 𝛽2𝐿𝑏𝑠𝑖𝑧𝑒𝑖𝑡 + 𝛽3𝐿𝑚𝑒𝑒𝑡𝑖𝑡 + 𝛽4𝐹𝑒𝑚𝑎𝑙𝑒𝑖𝑡 + 𝛽5𝐶𝑒𝑜𝑑𝑢𝑎𝑙𝑖𝑡
+ 𝛽6𝐿𝑓𝑠𝑖𝑧𝑒𝑖𝑡 + 𝛽7𝐹𝑖𝑥𝑒𝑑_𝑎𝑠𝑠𝑒𝑡𝑠𝑖𝑡 + 𝛽8𝑅𝑂𝐴𝑖𝑡
+ 𝛽9𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦𝑖𝑡 + 𝛽10𝑆𝑎𝑙𝑒𝑔𝑟𝑜𝑤𝑡ℎ𝑖𝑡 + 𝜀𝑖𝑡
(4)
Model (2) is structured as follows:
𝐿𝑒𝑣𝑖𝑡 = 𝛽1 + 𝛽2𝐿𝑏𝑠𝑖𝑧𝑒𝑖𝑡 + 𝛽3𝐿𝑚𝑒𝑒𝑡𝑖𝑡 + 𝛽4𝐹𝑒𝑚𝑎𝑙𝑒𝑖𝑡 + 𝛽5𝐶𝑒𝑜𝑑𝑢𝑎𝑙𝑖𝑡
+ 𝛽6𝐿𝑓𝑠𝑖𝑧𝑒𝑖𝑡 + 𝛽7𝐹𝑖𝑥𝑒𝑑_𝑎𝑠𝑠𝑒𝑡𝑠𝑖𝑡 + 𝛽8𝑅𝑂𝐴𝑖𝑡
+ 𝛽9𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦𝑖𝑡 + 𝛽10𝑆𝑎𝑙𝑒𝑔𝑟𝑜𝑤𝑡ℎ𝑖𝑡 + 𝜇𝑖 + 𝜀𝑖𝑡
(5)
Table 6. Regression results for dynamic Models
Variable POLS-dynamic
(1)
Fixed-dynamic
(2)
GMM-Dynamic 1
(3)
GMM-Dynamic 2
(4)
GMM-Dynamic 3
(5)
L.Lev 0.820***
[0.000]
0.410***
[0.000]
0.752***
[0.000]
0.759***
[0.000]
0.705***
[0.000]
Lbsize -0.010
[0.330]
-0.012
[0.480]
-0.006
[0.820]
-0.021
[0.430]
0.001
[0.980]
Notes: *, **, and *** correspond to the 10%, 5%, and 1% levels of significance, respectively.
Hoang Mai Phuong and Nguyen Thanh Hong An
92
Table 6. Regression results for dynamic Models (cont.)
Variable POLS-dynamic
(1)
Fixed-dynamic
(2)
GMM-Dynamic 1
(3)
GMM-Dynamic 2
(4)
GMM-Dynamic 3
(5)
Lmeet -0.002
[0.520]
-0.002
[0.660]
-0.019**
[0.040]
-0.015
[0.012]
-0.014*
[0.090]
Female -0.003
[0.130]
-0.001
[0.840]
-0.018**
[0.020]
Female_% -0.103**
[0.020]
Female_dummy -0.032**
[0.030]
Ceodual 0.005
[0.270]
-0.003
[0.710]
-0.010
[0.340]
-0.008
[0.420]
-0.009
[0.410]
Lfsize 0.012***
[0.000]
0.078***
[0.000]
0.044***
[0.000]
0.045***
[0.000]
0.036***
[0.000]
Fixed_assets -0.069***
[0.000]
-0.120***
[0.000]
-0.080*
[0.070]
-0.076*
[0.090]
-0.081**
[0.050]
ROA -0.283***
[0.000]
-0.415***
[0.000]
-0.218**
[0.050]
-0.226**
[0.050]
-0.347***
[0.000]
Liquidity -0.008***
[0.000]
-0.013***
[0.000]
-0.001
[0.510]
-0.001
[0.680]
-0.002
[0.340]
Salegrowth -0.001
[0.420]
0.001
[0.330]
0.003
[0.190]
0.002
[0.240]
0.003
[0.160]
Constant -0.083**
[0.040]
-1.221***
[0.000]
0.000
[.]
-0.709***
[0.000]
0.000
[.]
Year dummies Yes Yes Yes Yes Yes
Industry dummies Yes No Yes Yes Yes
Number of
observations
1,393 1,393 1,393 1,393 1,393
R2 0.875 0.448 - - -
F statistic (or
Wald statistic)
453.598 34.288 29,205.64 15,832.75 30,388.74
P-value 0.000 0.000 0.000 0.000 0.000
Number of
instruments
87.000 87.000 87.000
Arellano-Bond
AR(1) (P-value)
0.000 0.000 0.000
Arellano-Bond
AR(2) (P-value)
0.630 0.562 0.562
Hansen J (P-
value)
0.288 0.312 0.312
Notes: *, **, and *** correspond to the 10%, 5%, and 1% levels of significance, respectively.
DALAT UNIVERSITY JOURNAL OF SCIENCE [ECONOMICS AND MANAGEMENT]
93
The analysis is performed on 1,592 observations collected from 199 nonfinancial
companies listed on the Ho Chi Minh City Stock Exchange from 2012 to 2019. The use
of lagged variables (1 period) reduces the number of observations to 1393. Lev is the
dependent variable, defined as the ratio of total debt to total assets of the business. L.Lev
is lagged one period from Lev. Bsize is the number of board members. Meet is the number
of board meetings per year. Female is the number of female members of the Board of
Directors. Female_% is the percentage of female directors. Female_dummy is an
indicator variable equal to 1 if the board has female members, and 0 if not. Ceodual is an
indicator variable equal to 1 if the chairman of the BOD also holds the position of CEO,
and 0 if not. Lfsize is the logarithm of the firm's total book value. Fixed_assets is the ratio
of fixed assets to total assets. ROA is the ratio of after-tax profit to total assets. Liquidity
is the ratio of short-term assets to short-term liabilities. Salegrowth is the rate of sales
growth. Column (1) presents the regression results for Model (3), using the POLS method.
Column (2) presents the regression results for Model (3) using the regression method with
fixed effects. Column (3) presents the regression results for Model (3) using the system
GMM method. Column (4) presents the regression results for Model (3) using the system
GMM method and variable Female_% replacing variable Female. Column (5) presents
the regression results for Model (3) using the system GMM method and variable
Female_dummy replacing variable Female.
Model (3) is structured as follows:
𝐿𝑒𝑣𝑖𝑡 = 𝛽1 + 𝛽2𝐿. 𝐿𝑒𝑣𝑖𝑡 + 𝛽3𝐿𝑏𝑠𝑖𝑧𝑒𝑖𝑡 + 𝛽4𝐿𝑚𝑒𝑒𝑡𝑖𝑡 + 𝛽5𝐹𝑒𝑚𝑎𝑙𝑒𝑖𝑡
+ 𝛽6𝐶𝑒𝑜𝑑𝑢𝑎𝑙𝑖𝑡 + 𝛽7𝐿𝑓𝑠𝑖𝑧𝑒𝑖𝑡 + 𝛽8𝐹𝑖𝑥𝑒𝑑_𝑎𝑠𝑠𝑒𝑡𝑠𝑖𝑡
+ 𝛽9𝑅𝑂𝐴𝑖𝑡 + 𝛽10𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦𝑖𝑡 + 𝛽11𝑆𝑎𝑙𝑒𝑔𝑟𝑜𝑤𝑡ℎ𝑖𝑡 + 𝜇𝑖 + 𝜀𝑖𝑡
(6)
5. CONCLUSIONS
This study examines the impact of BOD characteristics on the financial leverage
ratio of nonfinancial companies listed on the Ho Chi Minh City Stock Exchange for eight
years from 2012 to 2019. The dataset includes 199 companies with 1,592 observations.
The system GMM used to analyze the data helps avoid correlation between the
instrumental variables and the errors, overcomes the endogeneity problem, and estimates
the regression coefficients consistently.
The research results show that, in general, the characteristics of the BOD have an
impact on a firm’s financial leverage decisions, with two out of four variables reflecting
the characteristics of the BOD being statistically significant. Specifically, the number of
meetings of the BOD during the year has a negative and statistically significant
coefficient. This shows that companies with more active BODs, as indicated by the
number of meetings per year, often control their leverage at a lower level than firms with
less active boards. In addition, board gender diversity, whether defined by the number of
female directors, the proportion of female directors, or the presence of female directors,
is negatively correlated with financial leverage and remains statistically significant at 5%
in all three specifications. This can be explained by a higher level of risk aversion among
women, which influences board decisions regarding the level of borrowing, resulting in
Hoang Mai Phuong and Nguyen Thanh Hong An
94
lower debt ratios compared to boards without female directors. The size of the BOD and
CEOs with dual roles were found to have no impact on the leverage ratio.
This result complements and follows previous studies on the influence of
governance structure on corporate capital structure, which is the basis for providing
suggestions to managers on operating and managing their businesses.
ACKNOWLEDGMENTS
This research is funded by a Dalat University research grant.
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