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THE IMPACTS OF CEO GENDER ON NON-PERFORMING
LOAN RATIO IN VIETNAM BANKING SECTOR
TÁC ĐỘNG CỦA GIỚI TÍNH CEO ĐẾN TỶ LỆ NỢ XẤU
TRONG NGÀNH NGÂN HÀNG Ở VIỆT NAM
PhD, Nguyen Thanh Dat; MA, Vuong Bao Bao
The University Of Danang - University Of Economics
datnt@due.udn.vn
Abstract
This paper aims to analyse the relationship between CEO’s gender and the non-performing
loan ratio of commercial banks in Vietnam. We employ a data set which includes 30 Vietnam
commercial banks over the period 10 years from 2008 to 2018. To investigate the effect of CEO
gender on credit risk, we run a fixed effect multivariate regression model in which the dependent
variable is the non-performing loan measured by the sum of all debts categorized in group 3, 4
and 5. The main interested variable is CEO gender which takes the value of 1 if the CEO is a
male and 0 otherwise. On average, banks with male CEO have a lower non-performing loan
ratio than banks with female CEO. CEO gender coefficient is statistically significant our main
regression model. The negative relationship between CEO gender and non-performing loan are
consistent through another two robustness tests, namely controlling for GDP growth rate and
controlling for financial crisis period.
Keywords: Banking sector, CEO gender, Non-performing loan, Fixed effect model,
Financial crisis
Tóm tắt
Nghiên cứu này nhằm phân tích mối quan hệ giữa giới tính của CEO và tỷ lệ nợ xấu
của các ngân hàng thương mại tại Việt Nam. Chúng tôi sử dụng tập dữ liệu bao gồm 30 ngân
hàng thương mại Việt Nam trong 10 năm từ 2008 đến 2018. Để điều tra ảnh hưởng của giới
CEO đối với rủi ro tín dụng, chúng tôi sử dụng mô hình hồi quy đa biến có hiệu ứng cố định
trong đó biến phụ thuộc là lệ nợ xấu được đo bằng tổng của tất cả các khoản nợ được phân
loại trong nhóm 3, 4 và 5. Biến quan tâm chính là giới tính của CEO, lấy giá trị bằng 1 nếu
CEO là nam và 0 nếu ngược lại. Tính trung bình, các ngân hàng có CEO là nam có tỷ lệ nợ
xấu thấp hơn các ngân hàng có CEO là nữ. Hệ số của giới tính của CEO có ý nghĩa thống kê
trong mô hình hồi quy chính của chúng tôi. Mối quan hệ tiêu cực giữa giới tính của CEO và
khoản nợ xấu nhất quán thông qua hai bài kiểm tra bền vững, đó là kiểm soát tốc độ tăng
trưởng GDP và kiểm soát cho thời kỳ khủng hoảng tài chính.
Từ khóa: Ngành ngân hàng, giới tính CEO, nợ xấu, mô hình hiệu ứng cố định, khủng
hoảng tài chính
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1. Introduction
Non-performing loans (NPLs) are always long-standing problems for the banking sector
because of their potential risks of losing credit. Once a loan is classified as an NPL, full debt
recovery is rarely feasible and it is difficult and time-consuming to retrieve the debt. The
appearance of bad debts is not only due to the customer side and the economic condition but
also due to banks’ management practices.
Increasing in the level of non-performing loans poses a significant risk to the banking
sector in particular and the entire financial sector in general. Failure in controlling non-
performing loans over a long period gradually negatively affects banks’ profitability of
commercial banks (Kaaya and Pastory, 2013). Consequently, the increase of non-performing
loans usually results in high loan provisioning, which leads to a drop in profits of banks
(Kithinji, 2010) and gradually dimishing the capability of bank sector in contributing to the
development of the economy (Abd Karim et al., 2010). Unfortunately, Vietnam’s banking
sector is alarmed with the rising of non-performing loans. In 2018, NPLs account for 4.6%
of outstanding loans, which is nearly doubled compared to 2017 (2.6 %). Therefore,
researches that are undertaken to investigate factors that affect banks’ non-performing loan
ratio are necessary.
The main purpose of the research is analysing the relationship between CEO’s gender
and the non-performing loan ratio of commercial banks in Vietnam. The answer to this
research question is crucial to all stakeholders including administrative boards, investors and
policymaker. First, understand this relationship helps boards have an appropriate risk
management strategy including appointing male or female CEO. As in the literature review
below, women could be more risk averse, or less risk averse. Secondly, knowing this
relationship helps investors in choosing their investment portfolio. Finally, policymakers
know which banks are potentially riskier than the other.
In this paper, we employ a data set which includes 30 Vietnam commercial banks over
the period 10 years from 2008 to 2018. To investigate the effect of CEO gender on credit
risk, we run a fixed effect multivariate regression model. Our dependent variable is the non-
performing loan ratio, measured by the ratio of all debts in group 3, 4 and 5 (according to the
State Bank of Vietnam, 2015 and 2017) over total outstanding loans. The independent variable
is CEO gender which takes the value of 1 if the CEO is a male and 0 otherwise. In order to
further the analysis, we also test the impact of CEO gender on banks’ non-performing loan
ratio by undertaking two robustness tests namely: (i) controlling for macroeconomic condition
expressed by GDP growth rate and (ii) controlling for financial crisis.
The rest of paper is organised as followed. Section 2 discusses the literature review.
Section 3 describes our research data set and research methodology. Section 4 provides the
main regression results and robustness tests’ results. Finally, Section 5 sets forth the
conclusion remarks.
2. Literature review
Related to social belief and leadership style, Vu et al. (2017) illustrate that the press
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demonstrates and builds up strong gender stereotyping in Vietnam society about male and
female directors. Compared to male managers, female managers are thought to be better in
social relationship as they are caring, friendly, flexible, and careful. These differences are
likely to result in the discrepancy in making business choices.
In a study about female administration in Vietnamese small companies, Vo and Harvie
(2009) find that female managers had trouble when dealing with financing, being aware of
the legal framework and business laws, taking advantage of technology, use of IT and seeking
support from the local government. However, Pham and Talavera (2018) do not observe the
same thing as been shown that if a firm is administrated by a woman, it is more likely to be
granted a bank loan. Not to mention that that firm also have better link to the business
community.
Research on gender and risk-taking behaviour generally varies. A gender-specific
difference in risk aversion has been confirmed by researches, both in psychological and
economic studies.
On the one hand, some researches illustrate that women are likely to be more risk averse
than men (Barber and Odean, 2001). This risk aversion can be found in a variety of studies’
form, ranging from empirical evidences (Barber and Odean, 2001; Croson and Gneezy, 2009).
More specifically, Barber and Odean (2001) found that by observing trading behaviour of
both genders, they found that women are more risk averse. This is still true when women and
men’s behaviour are analysed through a common investment game (Charness and Gneezy,
2012). Moreover, this characteristic of women is also be found in their individuals’ asset
management. Finucane et al. (2000) illustrates that women usually tries to get rid of risky
assets. Sunden and Surette (1998) confirms this and emphasise this is especially true when
women are in their single period. Jianakoplos and Bernasek (1998) also noted that single
women are in favour of avoiding risks when they conduct wealth allocation. Generally, Powell
and Ansic (1997), in his study, states that among factors which can show the difference
between men and women, only risk aversion stands out to be affected by the gender factor.
Specifically, in banking sector, a few studies have tried to determine the effect of gender
on the performance of banks. Some researches focus on the gender of loan officers and find
that the default rates of loans originated by women are lower than men’s (Beck et al., 2009).
This finding is consistent with the view that women take lower risk than men. In the context
of bank firm relationships, Bellucci et al. (2010) also find that women are more risk averse
and less self-confident.
In addition, when women are members of the board of directors, research shows that
they take their supervisory role very seriously. Lenard et al. (2014) studied the board of
directors in all firms except in the financial sector and found that a higher proportion of
women on board were associated with lower variation in market returns of stock. Robinson
and Dechant (1997) note that female directors are said to work harder, with better
communication skills, contributing to a better overall board problem-solving ability. Eagly
and Carli (2003) suggest that women must demonstrate additional capacity to reach
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directorship, which implies that women are quite diligent in the role of director.
On the other hand, compared to women, men could be more risk averse (Iqbal et al.,
2006; Adams and Funk, 2012; Sapienza et al., 2009, Berget et al., 2014). More specifically,
related to stock option awards, Iqbal et al. (2006) analysed risk attitudes of male and female
executives and they found that the selling behaviour of male executives consists of more risk
than what was done by the female ones. In addition, Adams and Funk (2012) provided
evidence that women who are directors may not always be the same as the majority, in this
case, female board members are more risk loving than their male counterparts. Using survey
data, Adams and Funk (2012) demonstrate that female directors are less risk averse than their
male counterparts, as opposed to overall demographic results. Sapienza et al. (2009) provide
evidence of the biological basis for career options in the financial sector. They examined a
sample of MBA students from the University of Chicago and found that women with higher
circulating testosterone levels were associated with a reduced risk aversion. Those in the
study with high testosterone and low risk aversion were more likely to pursue a financial
career, even when the gender has been controlled. Berger et al. (2014) examines the effect of
board member characteristics on risk taking in German banks over the period 1994-2010.
They consider portfolio risk as measured by two indicators: risk-weighted asset-to-total asset
ratio and Herfindahl-Hirschman index of loan portfolio concentration. By studying the
composition of the executive board in the banking industry, they found that changes in board
of directors resulting in a higher proportion of female members increase their portfolio risk,
both in two measures. They found that higher risk taking was associated with the young age
of executives, lower percentage of executives with a Ph.D. and - last but not least – female’s
existence in the board. Therefore, they support the view that the presence of women in
management comes with risks. However, it should be emphasised that they consider the two
measures related to portfolio risk and that the proportion of women on the board of directors
in their sample is very low (about 3%).
Last but not least, there have been some neutral findings which tell no difference
between men and women. Maxfield et al. (2010) examines risk trends and decision-making
skills of female managers. Their survey found that the motivations for women to take risks
are the same as the motivations identified in the study as gender blind in general. In the
financial sector, Bliss and Potter (2002) found no difference in risk-taking between male and
female mutual fund managers. Atkinson et al. (2003) found that male and female fixed income
mutual fund managers did not differ significantly in performance or risk. Zigraiova (2015)
studies how the composition of banks’ management board may influence their risk-taking
behaviour for some banks in the Czech Republic in the period 2001-2012. She examines the
effect of female directors in addition to the average age, the proportion of non-national
directors and director education. Risk is measured by four variables: z-score, bad debt ratio,
profit volatility, and ratio of liquid assets to deposits and short-term financing. She found
mixed evidence on the impact of female directors on the risk-taking behaviour of banks.
Different results for different types of Czech banking (building societies, commercial
banking) and different risk variables.
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3. Researh methodology
3.1. Data
The data used by the author is a secondary data source. The secondary data source used
is the data collected from Finpro and the Annual Consolidated Financial Statements (including
Balance Sheet, Production and Business Report, Cash Flow Report currency and financial
statement) of 30 Vietnamese commercial banks over 10 years from 2008 to 2018.1
3.2. Regression model
To investigate the effect of CEO gender on credit risk, we run the following multivariate
regression model:
= + + (1)where NPL is non-performing loan ratio measured by the ratio of all debts
in group 3, 4 and 5 (according to the State Bank of Vietnam, 2015 and 2017) over total
outstanding loans. Our main interested variable is CEO which takes the value of 1 if the CEO
is a male and 0 otherwise. Following the previous literature (Huang and Kisgen, 2013), our
control variables include LOAN measured natural logarithm of total loans, equity on asset
ratio EA, bank size measured by the natural logarithm of total assets and return to asset ratio.
Model (1) is also controlled for bank fixed effects .
4. Results
4.1. Descriptive Statistics and correlations matrix
Table 1 provides some descriptive statistics including the mean, maximum, minimum,
standard deviation and the number of observations for each variable.
Table 1: Data description
Standard
Variables Mean Maximum Minimum Observations
Deviation
NPL 0.018 0.11 0 0.016 330
CEO 0.88 1 0 0.32 311
LOAN 0.52 0. 82 0.11 0.17 330
EA 0.10 0. 46 0.03 0.06 330
SIZE 31.92 34.81 28.51 1.22 320
ROA 0.0087 0.0595 -0.0599 0.0086 330
Source: Author’s calculations.
From Table 1, we can observe that the NPL ranges between 0 and 0.11 with the mean
value of 0.018 and standard deviation of 0.016 indicating low variance. The average value
of CEO is recorded at 0.888 which means that there are more male CEOs than female ones,
standard deviation of 0.32. As for the control variables, LOAN ranges between 0.11 and 0.82
with an average of 0.52 and standard deviation of 0.17 signifies a low variance. On the other
hand, the average of EA recorded at 0.10 with a range of 0.03 and 0.46, standard deviation
of 0.06. The SIZE ranges between 28.51 and 34.81 with an average of 31.92 and standard
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deviation of 1.22 signifies a high variance. Lastly, ROA ranges between -0.0599 and 0.0595
with an average of 0.0087 and standard deviation of 0.0086 signifies a low variance.
Table 2: Correlation Matrix
NPL CEO LOAN EA SIZE ROA
NPL 1.000
CEO -0.069 1.000
LOAN 0.068 -0.164 1.000
EA 0.011 -0.065 -0.108 1.000
SIZE 0.046 0.178 0.213 -0.711 1.000
ROA -0.102 -0.007 0.071 0.320 -0.246 1.000
Source: Author’s calculations.
Table 2 reports the correlation coefficients between all variables in (1). As we can see
from Table 2, no coefficient is greater than 0.7. This means our research model is free from
the multicollinearity problem.
4.2. Empirical Results and Analysis
Table 3: Main regression results
VARIABLES NPL
CEO -0.00723*
(0.00368)
LOAN -0.00466
(0.0105)
EA 0.0682**
(0.0288)
SIZE 0.00355*
(0.00200)
ROA -0.234*
(0.122)
CONSTANT -0.0903
(0.0648)
Observations 303
R-squared 0.046
Standard errors in parentheses. *** p
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performing loan on total loans of banks with male CEOs is lower than those with female
CEOs by a value off 0.00723. Among the control variables, ROA and SIZE are both
statistically significant at 10% level while LOAN is statistically insignificant. In detail, EA
has a positive impact on NPL. The coefficient of EA is 0.0682 which shows that when equity
to total assets ratio increases by one percent the non-performing loan ratio increases by 0.0628
percent. On the other hand, bank size has the positive effect on NPL. When SIZE increases
by one unit, NPL increases by 0.00355. However, ROA negatively influences NPL, with its
coefficient being -0.234.
4.3. Robustness tests
To make the conclusions more convincing, the authors continues to test the impact of
CEO gender on banks’ non-performing loan ratio by undertaking two robustness tests namely:
(i) controlling for macroeconomic condition expressed by GDP growth rate and (ii)
controlling for financial crisis.
4.3.1. Controlling for GDP growth rate
In the first robustness test, we regress the following model:
NPLit = a0 + a1CEOit + a2LOANit + a3EAit + a4SIZEit + a5ROAit + a6GDPit + ai + εit (2)
where GDP is the gross domestic product growth rate.
Table 4: Regression results which control GDP growth rate
VARIABLES NPL
CEO -0.00782**
(0.00358)
LOAN 0.00533
(0.0105)
EA 0.0770***
(0.0281)
SIZE 0.00672***
(0.00209)
ROA -0.248**
(0.118)
GDP -0.573***
(0.139)
CONSTANT -0.163**
(0.0653)
Observations 303
R-squared 0.103
Standard errors in parentheses. *** p
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The results of regression (2) are displayed in Table 4. Consistent with our main results,
CEO has a negative impact on NPL. The coefficient CEO is statistically significant at 5%.
Similarly, the equity to asset ratio and bank size positively associate with bank’s non-
performing loan ratio, while ROA negatively affects NPL. On the other hand, GDP growth
rate shows its important role in affecting banks risk performance. GDP is statistically
significant at 1% level and has a negative impact on NPL. In detail, if GDP growth rate
increases by one percentage point, banks’ non-performing loan ratio decreases by 0.573
percentage point on average.
4.3.2. Controlling for financial crisis
The global financial crisis highlighted the importance of effective corporate governance
in managing bank risk (Peni and Vahama, 2012; Pathan and Faff, 2013). Francis et al. (2015),
note that the role of boards would be more important and thus more visible in terms of bank
performance. In a study of banks with female CEOs, Palvia et al. (2015) provide evidence
that for smaller banks, those with female CEOs and female board chairs were less likely to
fail during the financial crisis. Therefore, in order to further our analysis, we also control for
financial crisis periods into our main model of specification:
= + + (3)where CRISIS is measured in binary data, the year with crisis takes the
value of 1 and 0 otherwise. In our sample period, the year of 2008 and 2009 are recorded as
financial crisis period.
Table 5: Regression results which control financial crisis
VARIABLES NPL
CEO -0.00673*
(0.00359)
LOAN 0.00509
(0.0105)
EA 0.0427
(0.0288)
SIZE -0.00392
(0.00272)
ROA -0.169
(0.120)
CRISIS -0.0144***
(0.00365)
CONSTANT 0.147*
(0.0871)
Observations R-squared 303
R-squared 0.099
Standard errors in parentheses *** p
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Table 5 reports the results of regression (3). The results show that our previous analysis
is hold. CEO have a negative impact on NPL and its coefficient is statistically significant at
10% confident level. Interestingly, during financial crisis the non-performing loan ratio is
lower that during non-crisis period. This result may be due to the fact that banks are more
cautious with their lending activity during the period of crisis.
5. Conclusion remarks
This paper aims to analyse the relationship between CEO’s gender and the non-
performing loan ratio of commercial banks in Vietnam. We employ a data set which includes
30 Vietnam commercial banks over the period 10 years from 2008 to 2018. To investigate
the effect of CEO gender on credit risk, we run a fixed effect multivariate regression model
in which the dependent variable is the non-performing loan ratio and the main interested
variable is CEO gender.
Our results show that male CEO, i.e. CEO takes value of 1, has a negative impact on
NPL and its coefficient is statistically significant at 10% level in our main regression model.
On average the ratio of non-performing loan on total loans of banks with male CEOs is lower
than those with female CEOs by a value off 0.00723. The negative relationship between CEO
gender and non-performing loan are consistent through another two robustness tests, namely
controlling for GDP growth rate and controlling for financial crisis period.
Based on the findings from the empirical analysis, boards of directors may adjust their
management strategy including appointing male or female CEO. Also, investors can use these
results in managing their investment portfolio, based on their risk appetite. Last but not least,
policymakers, to some extent, may determine which banks are potentially riskier than the
other.
1
List of 30 banks using in this study:
Symbol Description
ABBank An Binh Commercial Joint Stock Bank
ACB Asia Commercial Bank
BAB Bac A Commercial Joint Stock Bank
BAOVIET Bank Bao Viet Joint Stock Commercial Bank
BID Bank for Investment and Development of Viet Nam
CTG Vietnam Joint Stock Commercial Bank for Industry and Trade
DongA Bank Dong A Commercial Joint Stock Bank
EIB Vietnam Export Import Commercial Joint Stock Bank
HDB Ho Chi Minh City Development Joint Stock Commercial Bank
KLB Kien Long Commercial Joint Stock Bank
LPB LienViet Post Joint Stock Commercial Bank
Maritime Bank Vietnam Maritime Commercial Joint Stock Bank
MBB Military Commercial Joint Stock Bank
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NamABank Nam A Commercial Joint Stock Bank
NVB National Citizen Commercial Joint Stock Bank
PG Bank Petrolimex Group Commercial Joint Stock Bank
PvcomBank Vietnam Public Joint Stock Commercial Bank
Saigonbank Saigon Bank for Industry and Trade
SCB Sai Gon Commercial Joint Stock Bank
SeABank Southeast Asia Joint Stock Commercial Bank
SHB Saigon – Hanoi Commercial Joint Stock Bank
STB Sai Gon Thuong Tin Commercial Joint Stock Bank
TCB Vietnam Technological and Commercial Joint Stock Bank
TPB Tien Phong Commercial Joint Stock Bank
VBB Vietnam Thuong Tin Commercial Joint Stock Bank
VCB Joint Stock Commercial Bank for Foreign Trade of Vietnam
VIB Vietnam International Commercial Joint Stock Bank
VietABank Viet A Commercial Joint Stock Bank
VietCapital Bank Viet Capital Commercial Joint Stock Bank
VPB Vietnam Prosperity Joint Stock Commercial Bank
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