Layout 1 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 Finan[.]
INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 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 xem xét mối quan hệ vị trí địa lý doanh nghiệp sách cổ tức Theo lý thuyết đại diện, cơng ty có vị trí địa lý xa đưa mức cổ tức cao để giảm xung đột lợi ích cổ đơng ban giám đốc Sự xung đột đến từ khó khăn cổ đơng việc quan sát định ban giám đốc Bên cạnh đó, theo lý thuyết tín hiệu, bất cân xứng thông tin, công ty xa muốn giữ mức cổ tức cao để báo hiệu cho nhà đầu tư triển vọng họ Sử dụng liệu doanh nghiệp Việt Nam, nghiên cứu cho thấy kết thực nghiệm hỗ trợ cho giả thuyết Những kết đóng góp vào kết nghiên cứu trước vị trí địa lý hành vi doanh nghiệp, đặc biệt mối quan hệ vị trí địa lý sách cổ tức, vốn bị bỏ qua nghiên cứu Từ khố: hiệu ứng địa lý, vị trí doanh nghiệp, sách cổ tức Introduction The literature of corporate dividend policies shows that many studies explored firm-specific factors affecting the firms’ dividend policies Amidu 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 relationship to dividend level of enterprises Patra et al (2012), Bushra, and Mirza (2015), Khan & 1217 INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 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 behaviors 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 compensation, 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 potential 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 equity, and dividend can be used for this purpose Low free cash flow prevents the managers from getting opportunistic benefits Signaling theory also supports for relationship between firm location and dividend policies Remotely located firms pursue higher dividend policies to signal their prospects to less informed shareholders It is worth noting that in the real world, dividend cuts receive very unfavorable responses from the stock market (Miller and Rock, 1985; Kumar, 1988) If a firm is able to maintain its higher dividend policy, this means that the firm is confident of their future earnings I, therefore, argue that firms located in a remote area are likely to have higher dividends than those in central area This paper tries to find the empirical evidence for this hypothesis The study uses financial data and dividend history of all the companies listed on Hanoi and Hochiminh stock exchanges till the end of the August 2020 (669 companies) Using both univariate and multivariate analyses, the results show that centrally located firms have lower dividend level To be specific, firms located in the two biggest cities in Vietnam (Hanoi and Hochim1218 INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 inh) have about two percent lower in dividend yield than the others on average Besides, firms located in five municipalities have roughly 2.6 percent lower in dividend yield than the others Finally, the study shows that distance to the nearest central city has positive correlation with firm’s dividend level These findings contribute to the literature of geographical effect on corporate behaviors, especially the relationship between firms’ location and dividend policies, which are overlooked in previous studies In next section, the paper is divided into five parts The first part presents data and methodology The next is the main findings Final part is conclusion Data The study use sample of all listed companies on Hanoi and Hochiminh stock exchanges Using sample of companies on the two biggest stock exchanges in Vietnam allows me to easily get financial information and dividend history The sample size is 669 companies with 6255 available observations List of companies as well as their information is collected on cophieu68.vn The time period of study is 2006 to 2019 I exclude firms in financial industries because of their difference in performance measures from non-financial firms All the observations before going listed are also excluded because there is no dividend information Variables I use three different proxies for location: First, I use a dummy variable named Center to explore the difference between firms located in the two main central cities (Hanoi and Hochiminh) and firms located in the other provinces Center is equal to if the firm located in Hanoi or Hochiminh, and otherwise Second, I use Municipality variable to separate the locations into municipalities and nonmunicipalities Municipality is also a dummy variable, which equals to if firm’s location is in Hanoi, Hochiminh, Haiphong, Danang and Cantho (top-five municipality in Vietnam), and otherwise Finally, I use a continuous variable named Distance to measure the distance from firm’s headquarter to the nearest central city (Hanoi or Hochiminh) Distance is measured by natural log of one plus the distance in kilometer from firm’s location to the nearest central city In terms of dividend, I use Dividend yield as the main variable Dividend yield is measured by cash dividend per share over share price in the same year multiplied by 100 This measure is motivated by the Sheng Yao, Wei-Wei Zhang & Chen-Miao Lin (2019) Besides, I also use other dividend measures, i.e Dividend payout ratio and dividend per share ratio Dividend payout ratio is calculated by dividend over net income multiplied by 100 Dividend per share ratio is calculated by dividend over par value multiplied by 100 Methodology The study uses both univariate and multivariate analyses For univariate analysis, I use mean difference test for difference in mean dividend yield, mean dividend payout ratio, and mean dividend per share ratio of firms in center and non-center, municipality and non-municipality, and firms with the distance above and below 75 percentile 1219 INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 For multivariate analysis, I try to control for other firm-specific characteristics, which may potentially affect dividend I also control for differences in dividend policy cross industries and years by adding industry and year dummy Since the main variables (Center, Municipality) are time invariant, the fixed-effect estimation is not applicable Thus, I use ordinary least squares (OLS) regression with robust standard errors clustered at the firm level for the following model: Yi = β0 + β1 Xi +β2 CVi +β3 industryi +β4 yeari + ε Where Y is dividend yield X is location variable; I would in turn add three location variables (Center, Municipality, Distance) to the model CV is a set of control variables, following Miller & Rock (1985), Kumar (1988), Uysal, Kedia, & Panchapagesan, (2008), Patra, Poshakwale & Ow-Yong (2012), Yao, Zhang & Lin (2019): 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 is the industry dummy variable, controlling for industry characteristics year is year dummy variable, controlling for year differences in dividend policy All the control variables are winsorized at the top and bottom one percentile values to minimize the effect of possibly spurious outliers in the data Findings and discussion Table 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 shows the firms in sample are diverse in total assets, 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 observations Sample includes all the companies listed in Hanoi and Hochiminh stock exchanges (669 companies) with 6255 observations Center is equal to if the firm located in Hanoi or Hochiminh, and otherwise Municipality equals to if firm’s location is in Hanoi, Hochiminh, Haiphong, Danang and Cantho, and 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 1220 INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 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 Center Municipality Distance (km) Mean 0.537 0.634 113 Standard deviation 0.498 0.481 200 Dividend yield 13.063 18.670 Dividend per share ratio 10.505 11.858 Dividend payout ratio Total asset (mil VND) Growth Fixed asset ROA 38.920 41.449 Min 0 0 0 Median 1 13.044 1.533 8.976 13.057 069 -.129 052 065 6255 60 20,421 582 820 4,642,286 1,313,771 6255 105.263 34.679 218.175 543,710 33,400,000 77,739 N 7.547 1,953,293 467,585 Max 6255 6255 6169 6255 6254 17.050 6245 319 6254 9,420,069 6210 a Univariate analysis Table presents the results for univariate analysis I found that comparing to remotely located 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 municipalities 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) Dividend 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 1221 INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 I find here suggest that firms’ location also affects the firms’ dividend policies There are, however, still many other factors, which potentially affect firms’ dividend I, therefore, employ multivariate 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 dividend measure, it provides the mean values for whole sample, subsamples of center and non-center, 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 Center Variable Whole Center sample Noncenter Municipality Difference Municipality Non- Difference Near to Far from municia central a central pality city city Difference Dividend 13.648 12.385 15.120 -2.734*** 12.460 Dividend 44.132 40.122 48.823 -8.701** (2.080) 44.675 43.197 Dividend 10.899 10.186 11.729 -1.543*** (3.681) 10.344 11.862 3969 2286 yield payout ratio per share ratio N 6255 3365 2890 (4.691) Distance 15.712 -3.251*** (5.391) 12.816 15.992 1.477 (0.341) 39.574 57.017 -17.442*** (3.678) -1.517*** (3.494) 10.693 11.479 -.786* (1.653) 4616 1639 -3.175*** (4.806) ***, **, * Significant at 1, 5, 10 percent levels, respectively b Multivariate analysis Table 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 1222 INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 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 if the firm located in Hanoi or Hochiminh, and otherwise Municipality is equals to if firm’s location is in Hanoi, Hochiminh, Haiphong, Danang and Cantho, and 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 variables 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) heteroskedasticity-consistent standard errors Dividend yield Center (t-statistic) Municipality Model (1) Model (2) -2.013*** (-2.71) -2.626*** (t-statistic) (-3.37) Distance (t-statistic) Model (3) 386*** (2.76) Size -3.863*** -3.908*** -3.840*** Growth 3.488*** 3.503*** 3.481*** -.292 -.355 -.378 (t-statistic) (t-statistic) Fixed assets (t-statistic) (-10.06) (-10.30) (9.53) (9.54) (-0.17) (-0.21) (-10.04) (9.51) (-0.22) ROA 53.536*** 53.370*** 53.559*** Constant 18.032*** 18.755*** 15.841*** 0.336 0.337 0.336 (t-statistic) (t-statistic) R2 N (10.02) (9.96) (3.18) (3.28) 6200 6200 ***, **, * Significant at 1, 5, 10 percent levels, respectively (9.99) (2.77) 6200 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 monitoring managers’ action require firms to set higher dividends to reduce agency conflicts Although 1223 INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 development of technology and the Internet have reduced information asymmetry between shareholders and managers, distance still prevents shareholders, who often live in big cities, from frequent 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 previously focuses on financial factors Amidu 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 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 significant to dividend policy setting The findings are consistent with the previous studies of relationship 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 necessary to balance dividend level and retained earnings so that firms can reduce the shareholdermanagers agency costs, and ensure capital needs for investment Conclusion According to agency theory and signaling theory, there is a relationship between firm location 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 correlation 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 necessary to balance dividend level and retained earnings so that firms can reduce the shareholdermanagers agency costs, and ensure capital needs for investment 1224 INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020 ICYREB 2020 REFERENCES Bushra, A., & Mirza, N (2015) The determinants of corporate dividend policy in Pakistan The Lahore Journal of Economics, 20(2), 77 Coval, J D., & Moskowitz, T J (1999) Home bias at home: Local equity preference in domestic portfolios The Journal of Finance, 54(6), 2045-2073 Dewasiri, N J., Koralalage, W B Y., Azeez, A A., Jayarathne, P G S A., Kuruppuarachchi, D., & Weerasinghe, V A (2019) Determinants of dividend policy: Evidence from an Emerging and Developing market Managerial Finance Jensen, M C (1986) Agency costs of free cash flow, corporate finance, and takeovers The American economic review, 76(2), 323-329 Kang, J K., & Kim, J M (2008) The geography of block acquisitions The Journal of Finance, 63(6), 2817-2858 Khan, F A., & Ahmad, N (2017) Determinants of dividend payout: an empirical study of pharmaceutical companies of pakistan stock exchange (PSX) Journal of Financial Studies & Research, 16 Knyazeva, A., Knyazeva, D., & Masulis, R W (2010, November) Local director talent and board composition In 23rd Australasian Finance and Banking Conference Kumar, P (1988) Shareholder-manager conflict and the information content of dividends The Review of Financial Studies, 1(2), 111-136 Miller, M H., & Rock, K (1985) Dividend policy under asymmetric information The Journal of finance, 40(4), 1031-1051 Patra, T., Poshakwale, S., & Ow-Yong, K (2012) Determinants of corporate dividend policy in Greece Applied Financial Economics, 22(13), 1079-1087 Uysal, V B., Kedia, S., & Panchapagesan, V (2008) Geography and acquirer returns Journal of Financial Intermediation, 17(2), 256-275 Yao, S., Zhang, W W., & Lin, C M (2019) Firm location and corporate dividend policy: evidence from China Applied Economics, 51(58), 6213-6234 1225 ... 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. .. firms’ dividend I, therefore, employ multivariate analysis to control for these factors Table 2: Geographical effect on dividend: Univariate analysis This table presents geographical effect on dividend. .. correlation with firm’s dividend level These findings contribute to the literature of geographical effect on corporate behaviors, especially the relationship between firms’ location and dividend