Tác động về địa lý đối với các chính sách cổ tức thực trạng tại các công ty Việt Nam

GEOGRAPHICAL EFFECT ON DIVIDEND POLICY
EVIDENCE FROM VIETNAMESE COMPANIES
TÁC ĐỘNG VỀ ĐỊA LÝ ĐỐI VỚI CÁC CHÍNH SÁCH 
CỔ TỨC THỰC TRẠNG TẠI CÁC CÔNG TY VIỆT NAM
MA, Vu Duc Kien
Academy of Finance 
vuduckien231@gmail.com
Abstract
This study examines the relationship between firm location and dividend. According to
agency theory, remotely located firms may use higher dividends to reduce shareholder-manager
agency conflicts because of obstacles in observing the managers’ actions. Besides, according to
signaling theory, due to information asymmetry, remotely located firms may remain their higher
dividend to signal their investors about their prospects. Using Vietnamese corporate data, my
study shows the empirical results supporting for this hypothesis. These findings contribute to the
literature of geography and corporate behaviors, especially the relationship between geography
and dividend policies, which are overlooked in previous studies.
Keywords: geographical effect, firms’ location, dividend policy
Tóm tắt
Nghiên cứu này xem xét mối quan hệ giữa vị trí địa lý của doanh nghiệp và chính sách cổ
tức. Theo lý thuyết đại diện, các công ty có vị trí địa lý xa có thể đưa ra mức cổ tức cao hơn để
giảm xung đột lợi ích giữa cổ đông và ban giám đốc. Sự xung đột này có thể đến từ khó khăn của
cổ đông trong việc quan sát các quyết định của ban giám đốc. Bên cạnh đó, theo lý thuyết tín
hiệu, do sự bất cân xứng thông tin, các công ty ở xa muốn giữ mức cổ tức cao hơn để báo hiệu
cho các nhà đầu tư về triển vọng của họ. Sử dụng dữ liệu của doanh nghiệp Việt Nam, nghiên
cứu này cho thấy các kết quả thực nghiệm hỗ trợ cho giả thuyết này. Những kết quả này đóng
góp vào các kết quả nghiên cứu trước đây về vị trí địa lý và hành vi của doanh nghiệp, đặc biệt
là mối quan hệ giữa vị trí địa lý và chính sách cổ tức, vốn bị bỏ qua trong các nghiên cứu này.
Từ khoá: hiệu ứng địa lý, vị trí doanh nghiệp, chính sách cổ tức
1. Introduction 
The literature of corporate dividend policies shows that many studies explored firm-specific
factors affecting the firms’ dividend policies. Amidu và Abor (2006) show that there is a positive
relationship between dividend payout ratio and profitability, cash flow as well as corporate income
tax. Corporate risk, growth rate and market to book value ratio, however, have a negative rela-
tionship to dividend level of enterprises. Patra et al (2012), Bushra, and Mirza (2015), Khan &
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Ahmad (2017) also found these relationships in their studies. It is noticeable that the previous
studies of dividend determinants merely focus on financial factors. Non-financial factors have
been unobserved.
Meanwhile, previous studies have shown significant effects between a firm’s location on
its behaviors as well as its stakeholders. Kang and Kim (2008) show that acquirers prefer local
targets because they can gain more from these transactions. These findings are also found by
Kedia et al (2008). Coval and Moskowitz (1999) also pointed out that fund managers and analysts
prefer local stocks because of the advantage of information. The relationship between the firm’s
position and the power of the CEO as well as the board composition has also been shown in
recent studies (Knyazeva et al, 2010). There have been many studies of geography and the be-
haviors of firms and of investors. Few studies, however, have studied the relationship between
firm location and dividend policy.
According to agency theory, the separation between shareholders and managers can create
shareholder-manager conflict, since their objectives are diverged. While shareholders would like
to maximize their values through positive net present value investments, managers are tempted
by utilities of empire building. Accordingly, managers can raise their financial benefits (i.e. com-
pensation, bonus) through increasing the firm’s size (overinvestment); even these actions can
damage the firm’s value (Jensen (1986). These problems will be more aggravated if there are po-
tential barriers preventing shareholders from monitoring managers’ actions. Location can be one
of these barriers. 
Distance may reduce the monitoring ability of shareholders through frequent or personal
contacts with managers. Investors often concentrate in big cities because these cities have high
population density with well-educated people. Also, financial systems are more developed in
these cities. Frauds in managerial reports can often exist, and frequent and face-to-face contacts
with managers can be helpful for shareholders to realize and detect these problems. Investors,
who often lives in big cities, therefore face difficulties to ensure that the investment decisions of
a remotely located firm’ managers precise. In addition, agency theory suggests that reducing free
cash flow in firms can help to mitigate the agency problem of eq ... om one percentile values to min-
imize the effect of possibly spurious outliers in the data.
5. Findings and discussion
Table 1 shows the summary statistics for firms in the sample. About 53.7% of firms in the
sample are located in Hanoi and Hochiminh city, and roughly 63.4 of firms have headquarters in
top-five central cities in Vietnam. This implies that firms listed in stock exchanges are mainly
located in big cities. On average, the distance of a firm to a main central city is 113 kilometers.
The farthest distance is 820 kilometers. In terms of dividend payment, the majority of firms in
the sample pays dividend with the dividend payout ratio ranging from zero percent to roughly
218%. Annual dividend per share ratio is also various, ranging from 0% to 60%.
As for other financial measures, Table 1 shows the firms in sample are diverse in total as-
sets, growth, fixed assets and profitability. The firms’ sizes is from 20,421 to 33,400,000 million
dong. All the firms in sample have positive growth rate (from 8.976% to 17.050%). The sizes of
fixed assets are very different. The smallest is 582 million dong, while the largest is 9,420,069
million dong. On average, the firms achieve 6.5% return on assets. 
Table 1: Summary statistics of the variables
This table reports summary statistics for all the variables used in this study. Each variable
is reported mean, standard deviation, minimum, median, maximum values, and number of ob-
servations. Sample includes all the companies listed in Hanoi and Hochiminh stock exchanges
(669 companies) with 6255 observations. Center is equal to 1 if the firm located in Hanoi or
Hochiminh, and 0 otherwise. Municipality equals to 1 if firm’s location is in Hanoi, Hochiminh,
Haiphong, Danang and Cantho, and 0 otherwise. Distance is the number of kilometers from firm’s
location to the nearest central city. Dividend yield is measured by cash dividend per share over
share price in the same year multiplied by 100. Dividend payout ratio is calculated by dividend
over net income multiplied by 100. Dividend per share ratio is calculated by dividend over par
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value multiplied by 100. Total assets and Fixed assets are in million dong; Growth measured by
natural log of sales; ROA measured by net income over total assets.
a. Univariate analysis 
Table 2 presents the results for univariate analysis. I found that comparing to remotely lo-
cated firms, centrally located firms have 2.7% less dividend yield, 1.5% less dividend per share
ratio on average; these differences are statistically significant at 1% level. Dividend payout of
centrally located firms is much lower than remotely located firms, with the difference of 8.7%
(at 5% level). The correlation between location and dividend is still robust when comparing mu-
nicipalities and the others. Dividend yield and dividend per share are still lower if firms are located
in municipalities, with the difference of 3.2 and 1.5 respectively (significant at 1% level). Divi-
dend payout ratio of remotely located firms is higher, but not statistically significant. Finally,
comparing the dividend level of firms close to central cities and firms far from central cities, all
dividend measures are negative and statically significant (at 1% level, except for dividend per
share ratio, which is at 10% level). To sum up, univariate analysis show that the firms’ dividend
level has a positive correlation with firms’ distance. 
These results support the literature of geography and firms’ and investors’ behaviors. Kang
and Kim (2008) show that acquirers prefer local targets because they can gain more from these
transactions. These findings are also found by Kedia et al (2008). Coval and Moskowitz (1999)
also pointed out that fund managers and analysts prefer local stocks because of the advantage of
information. The relationship between the firm’s position and the power of the CEO as well as
the board composition has also been shown in recent studies (Knyazeva et al, 2010). The findings
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Mean
Standard 
deviation
Min. Median Max. N
Center 0.537 0.498 0 1 1 6255
Municipality 0.634 0.481 0 1 1 6255
Distance (km) 113 200 0 0 820 6255
Dividend yield 13.063 18.670 0 7.547 105.263 6255
Dividend payout ratio 38.920 41.449 0 34.679 218.175 6169
Dividend per share
ratio
10.505 11.858 0 8 60 6255
Total asset (mil.
VND)
1,953,293 4,642,286 20,421 543,710 33,400,000 6254
Growth 13.044 1.533 8.976 13.057 17.050 6245
Fixed asset 467,585 1,313,771 582 77,739 9,420,069 6210
ROA .065 .069 -.129 .052 .319 6254
I find here suggest that firms’ location also affects the firms’ dividend policies. There are, how-
ever, still many other factors, which potentially affect firms’ dividend. I, therefore, employ mul-
tivariate analysis to control for these factors.
Table 2: Geographical effect on dividend: Univariate analysis
This table presents geographical effect on dividend with univariate analysis. For each div-
idend measure, it provides the mean values for whole sample, subsamples of center and non-cen-
ter, municipality and non-municipality, distance below and above 75 percentile. It also provides
difference in mean values between subsamples, number of available observations. T-statistics for
mean difference test are shown in parentheses. 
***, **, * Significant at 1, 5, 10 percent levels, respectively
b. Multivariate analysis 
Table 3 shows the results for regression. After controlling for firm-specific characteristics,
the result still shows a negative correlation between central location (both center and municipality
variables) and dividend level, and a positive correlation between distance and dividend level, all
else equal. All the coefficients are statistically significant at 1% level. First, the coefficient on
Center of -2.013 means that centrally located firms have about 2% lower in dividend yield than
remotely located firms. Second, the coefficient on Municipality of -2.626 means that firms located
in municipalities have about 2.6% lower in dividend yield than the others. Finally, the coefficient
on Distance of .386 means that the farther a firm’ location is, the higher its dividend yield, on
average. 
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Center Municipality Distance
Variable Whole
sample
Center Non-
center
Differ-
ence
Munici-
pality
Non-
munici-
pality
Difference Near to
a central
city
Far from
a central
city
Difference
Dividend 
yield
13.648 12.385 15.120 -2.734***
(4.691)
12.460 15.712 -3.251***
(5.391)
12.816 15.992 -3.175***
(4.806)
Dividend 
payout
ratio
44.132 40.122 48.823 -8.701**
(2.080)
44.675 43.197 1.477
(0.341)
39.574 57.017 -17.442***
(3.678)
Dividend
per 
share ratio
10.899 10.186 11.729 -1.543***
(3.681)
10.344 11.862 -1.517***
(3.494)
10.693 11.479 -.786*
(1.653)
N 6255 3365 2890 3969 2286 4616 1639
Table 3: Geographical effect on dividend: Regression analysis
This table presents geographical effect on dividend with regression analysis. The dependent
variable is Dividend yield, measured by cash dividend per share over share price in the same year
multiplied by 100. The main independent variables are Center, Municipality, Distance, examined
in model (1), (2), (3), respectively. Center is equal to 1 if the firm located in Hanoi or Hochiminh,
and 0 otherwise. Municipality is equals to 1 if firm’s location is in Hanoi, Hochiminh, Haiphong,
Danang and Cantho, and 0 otherwise. Distance is measured by natural log of one plus the distance
in kilometer from firm’s location to the nearest central city (Hochiminh or Hanoi). Control vari-
ables include: Size measured by natural log of total assets; Growth measured by natural log of
sales; Fixed assets measured by fixed assets over total assets; ROA measured by net income over
total assets. Industry and year dummies are included. Ordinary least squares (OLS) regressions
are reported. T-values reported in the parentheses are estimated using White’s (1980) het-
eroskedasticity-consistent standard errors. 
***, **, * Significant at 1, 5, 10 percent levels, respectively.
These results are consistent with univariate analysis’ results and with the hypothesis that
remotely located firms are likely to have higher dividends. Accordingly, the obstacles in moni-
toring managers’ action require firms to set higher dividends to reduce agency conflicts. Although
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Dividend yield Model (1) Model (2) Model (3)
Center -2.013***
(t-statistic) (-2.71)
Municipality -2.626***
(t-statistic) (-3.37)
Distance .386***
(t-statistic) (2.76)
Size -3.863*** -3.908*** -3.840***
(t-statistic) (-10.06) (-10.30) (-10.04)
Growth 3.488*** 3.503*** 3.481***
(t-statistic) (9.53) (9.54) (9.51)
Fixed assets -.292 -.355 -.378
(t-statistic) (-0.17) (-0.21) (-0.22)
ROA 53.536*** 53.370*** 53.559***
(t-statistic) (10.02) (9.96) (9.99)
Constant 18.032*** 18.755*** 15.841***
(t-statistic) (3.18) (3.28) (2.77)
R2 0.336 0.337 0.336
N 6200 6200 6200
development of technology and the Internet have reduced information asymmetry between share-
holders and managers, distance still prevents shareholders, who often live in big cities, from fre-
quent and personal contacts with managers. The process of frequent and face-to-face contacts
with managers can be very helpful for shareholders to realize and detect these frauds in managerial
reports as well as investment choices. 
These findings contribute to the literature of dividend policy’s determinants, which previ-
ously focuses on financial factors. Amidu và Abor (2006) show that there is a positive relationship
between dividend payout ratio and profitability, cash flow as well as corporate income tax. Cor-
porate risk, growth rate and market to book value ratio, however, have a negative relationship to
dividend level of enterprises. Patra et al (2012), Bushra, and Mirza (2015), Khan & Ahmad (2017)
also found these relationships in their studies. My study shows that the geographical effect is sig-
nificant to dividend policy setting. The findings are consistent with the previous studies of rela-
tionship between firms’ location and corporate behaviors.
The findings in this study have some implications for investors and firms. For investors,
when building a dividend prediction model to support for investment decisions, investors need
to include the location factor and other non-financial factors, which have proved effects through
empirical studies, in stead including only financial factors. Information asymmetry needs to be
taken in account when investing in remotely located firms. For remotely located firms, it is nec-
essary to balance dividend level and retained earnings so that firms can reduce the shareholder-
managers agency costs, and ensure capital needs for investment. 
6. Conclusion
According to agency theory and signaling theory, there is a relationship between firm lo-
cation and dividend. Because of obstacles in observing managers’ actions of remotely located
firms, these firms use higher dividends to reduce shareholder-manager agency conflicts. Besides,
due to information asymmetry, remotely located firms remain their higher dividend to signal their
investors about their prospects. Using Vietnamese corporate data, my study shows the empirical
results supporting for this hypothesis. Accordingly, firms in two biggest cities in Vietnam (Hanoi
and Hochiminh) have about two percent lower dividend yield than the others on average.
Besides, firms located in five municipalities have roughly 2.6 percent lower dividend yield
than the others. Finally, the study shows that distance to the nearest central city has positive cor-
relation with firm’s dividend yield. These findings contribute to the literature of geography and
corporate behaviors, especially the relationship between geography and dividend policies, which
are overlooked in previous studies.
The findings in this study have some implications for investors and firms. For investors,
when building a dividend prediction model to support for investment decisions, investors need
to include the location factor and other non-financial factors, which have proved effects through
empirical studies, in stead including only financial factors. Information asymmetry needs to be
taken in account when investing in remotely located firms. For remotely located firms, it is nec-
essary to balance dividend level and retained earnings so that firms can reduce the shareholder-
managers agency costs, and ensure capital needs for investment. 
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