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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, i[r]
(1)THE IMPACT OF BOARD STRUCTURE ON FINANCIAL LEVERAGE OF VIETNAMESE LISTED FIRMS
Hoang Mai Phuonga*, Nguyen Thanh Hong Ana
aThe Faculty of Economics and Business Administration, Dalat University, Lam Dong, Vietnam *Corresponding author: Email: phuonghm@dlu.edu.vn
Article history
Received: November 1st, 2020
Received in revised form: November 22nd, 2020 | Accepted: November 30th, 2020
Abstract
This study examines the impact of board structure on financial leverage decisions, as measured by the ratio of total debt to total assets, of nonfinancial firms listed on the Ho Chi Minh City Stock Exchange Based on a dataset of 1,592 observations collected from 199 firms for the period from 2012 to 2019, the analysis shows a correlation between board characteristics and a firm’s financial leverage Specifically, the higher the number of annual board meetings or the larger the number of female members on the board of directors, the lower the rate of financial leverage On the other hand, the size of the board and the presence of CEOs on the board not have a significant influence on financial leverage decisions A robust test using the system of generalized method of moments (GMM) to control for endogeneity generally confirms the results
Keywords: Agency theory; Board structure; Capital structure; Corporate governance;
Financial leverage; Vietnamese listed firms
DOI: http://dx.doi.org/10.37569/DalatUniversity.10.3.785(2020) Article type: (peer-reviewed) Full-length research article Copyright © 2020 The author(s)
(2)TÁC ĐỘNG CỦA CẤU TRÚC HỘI ĐỒNG QUẢN TRỊ LÊN ĐÒN BẨY TÀI CHÍNH CỦA CÁC DOANH NGHIỆP NIÊM YẾT TẠI
VIỆT NAM
Hoàng Mai Phươnga*, Nguyễn Thanh Hồng Âna
aKhoa Kinh tế - Quản trị kinh doanh, Trường Đại học Đà Lạt, Lâm Đồng, Việt Nam *Tác giả liên hệ: Email: phuonghm@dlu.edu.vn
Lịch sử báo
Nhận ngày 01 tháng 11 năm 2020
Chỉnh sửa ngày 22 tháng 11 năm 2020 | Chấp nhận đăng ngày 30 tháng 11 năm 2020
Tóm tắt
Nghiên cứu kiểm chứng tác động cấu trúc hội đồng quản trị tới định địn bẩy tài chính, cụ thể tỷ lệ tổng nợ tổng tài sản, doanh nghiệp phi tài niêm yết Sở giao dịch chứng khoán Thành phố Hồ Chí Minh vịng tám năm từ năm 2012 đến 2019 Dựa liệu gồm 1,592 quan sát thu thập từ 199 doanh nghiệp, kết quả phân tích cho thấy có mối tương quan đặc điểm hội đồng quản trị đòn bẩy tài chính Cụ thể, số lượng họp hội đồng quản trị hàng năm nhiều hay số lượng thành viên nữ hội đồng quản trị lớn tỷ lệ địn bẩy tài thấp Trong đó, quy mơ hội đồng quản trị vai trị kiêm nhiệm giám đốc điều hành khơng có ảnh hưởng đáng kể tới định tài Các phương pháp kiểm định tăng cường, mơ hình động và phương pháp ước lượng system-GMM sử dụng để ước lượng hệ số hồi quy, tăng tính chính xác khẳng định kết thu từ mơ hình nghiên cứu
Từ khóa: Cấu trúc hội đồng quản trị; Cấu trúc vốn; Công ty niêm yết Việt Nam; Đòn bẩy
tài chính; Lý thuyết người đại diện; Quản trị doanh nghiệp
DOI: http://dx.doi.org/10.37569/DalatUniversity.10.3.785(2020) Loại báo: Bài báo nghiên cứu gốc có bình duyệt
Bản quyền © 2020 (Các) Tác giả
(3)1 INTRODUCTION
In recent years, along with the development of the stock market in Vietnam, the number and quality of listed companies have continuously improved Today's businesses are not only larger in scale but also increasingly professional and diversified in their operations In this context, abundant capital is a prerequisite for businesses to maintain and expand production and to promptly meet their growth needs Therefore, choosing an appropriate capital structure is an important financial decision that businesses need to consider to achieve the expected performance
Corporate governance is an emerging field of research in Vietnam In recent years, empirical work has mainly focused on (1) finding the factors that affect the governance structure or capital structure, and (2) examining the effects of ownership structure, governance structure, and capital structure on financial performance, leaving the relationship between governance structure and capital structure underexplored Therefore, examining the relationship between the governance structure and capital structure of listed companies in Vietnam is necessary and can provide useful insights This is especially important in the context of an integrated economy, where Vietnamese businesses are in dire need of effective management strategies to improve competitiveness as well as corporate value
Based on the agency theory of Jensen and Meckling (1976), one of the fundamental theories of corporate governance, we argue that the characteristics of the agent, in this case the board of directors, can influence the financial decisions of businesses, particularly financial leverage decisions Using a dataset of 1592 observations collected from 199 nonfinancial companies listed on the Ho Chi Minh City Stock Exchange from 2012 to 2019, our research results show that, in general, the characteristics of the board of directors have an impact on the financial leverage ratio of businesses Firms with active boards of directors, represented by the number of meetings per year, often control their financial leverage at a lower level than businesses with less active boards of directors In addition, the more women present on the board of directors, the lower the level of leverage in general On the other hand, board size and the presence of the Chief Executive Officer (CEO) on the board have no significant impact on financial leverage
(4)The paper is organized as follows: First, we briefly present the basic theory and empirical studies on the relationship between governance structure and capital structure, from which research hypotheses are developed Then, the next section presents the research method and the Models Finally, we present and discuss the empirical results and their implications
2 LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT
2.1 Literature review
The seminal study of Modigliani and Miller (1958) is one of the initial studies on capital structure Their proposed theory is formulated in two important propositions related to firm value and the cost of capital Modigliani and Miller (1958) show that the use of debt gives owners a higher rate of return, and their later theory (Modigliani & Miller, 1963) shows that, with the existence of corporate income tax, the use of debt will increase the value of the business In other words, a reasonable level of financial leverage will satisfy the requirements of managers (about the value of the business) as well as those of shareholders (about income)
Following Modigliani and Miller’s (1963) research, a series of theories were built with different perspectives on the corporate capital financing Model Of these, the agency theory of Jensen and Meckling (1976) is one of the prominent theories on the relationship between optimal capital structure and governance structure in controlling conflicts of interest between shareholders and managers Conflicts of interest arise from the transfer of certain decision-making powers to the agent in the relationship between the principal (shareholders) and the agent (managers) Both sides want to advance their interests, and there are always reasons to believe that the agent does not always act in the best interest of the principal In other words, the managers will be motivated to use the resources of the business for their personal benefit instead of for the benefit of the shareholders
(5)structure Their research shows that executives strive to avoid debt, and when there is no demand from the shareholders, the debt ratio is kept at a lower-than-optimal level Consequently, if measures to minimize management entrenchment are applied, the leverage ratio tends to increase
Taking a closer look at the theoretical discussion on the relationship between managers and shareholders addressed by agency theory, the divergence of interests between shareholders and managers can be reduced by establishing effective monitoring mechanisms to limit the managers' self-interested behavior More specifically, the decision to borrow is one of the monitoring options that makes managers hesitate because this will place the business under the supervision of many outside parties Hence, understanding the effects of governance characteristics on capital structure decisions can help us understand effective monitoring mechanisms Empirical studies have shown that a number of corporate governance characteristics, including board size, frequency of meetings during the year, number of female members, and CEO duality, influence corporate debt decisions A brief discussion of board characteristics and their relationship to financial leverage is given below
2.1.1 Board size and capital structure decisions
A company's financing decisions are governed by its board of directors (BOD) The operational efficiency of the BOD is the key to the success of the business According to Adams and Mehran (2003), a large board of directors can effectively monitor the company's operations and provide better expertise On the other hand, Lipton and Lorsch (1992) claim that large boards perform less efficiently than small ones Board size should be limited to a maximum of ten members and boards with eight or nine members is the most reasonable
Existing studies on the relationship between board size and financial leverage yield inconclusive findings Berger et al (1997) and Anderson, Mansi, and Reeb (2004) find a significant, negative correlation between board size and financial leverage Conversely, the studies of Kyereboah-Coleman and Biekpe (2006), Abor (2007), Bokpin and Arko (2009), and Rose, Munch-Madsen, and Funch (2013) find a positive relationship between board size and the capital structure of businesses This shows that the effectiveness of the board in monitoring management behaviors can directly, or indirectly, improve a company's access to debt Finally, Wiwattanakantang (1999) and Wen, Rwegasira, and Bilderbeek (2002) find no relationship between board size and financial leverage
2.1.2 Number of board meetings and a firm’s financial leverage
(6)Chidambaran (2007) and Ntim and Osei (2011) find a positive relationship between the frequency of board meetings and firm performance Regular BOD meetings often tend to produce better financial performance (Johl, Kaur, & Cooper, 2015) and the number of board meetings should be at least four meetings each year according to Eluyela et al (2018) Buchdadi, Ulupui, Dalimunthe, & Pamungkas (2019) and Kajananthan (2012) indicate that regular board meetings can lead to more debt decisions, thereby taking advantage of outside capital to modernize, expand, exploit investment opportunities, and increase the market value of the business The study also uncovers the important role of supervision through board meetings in agency theory Firms with high leverage are also likely to have more frequent board meetings (Al-Najjar, 2011) A study by Francis, Hassan, and Wu (2015) finds that companies with infrequent board meetings performed significantly worse during the financial crisis Stephanus, Anastasia, and Toto (2014) find a significant negative relationship between the frequency of board meetings and debt ratio Frequent board meetings can increase costs, time, and administrative support requirements for a company
2.1.3 Board gender diversity and a firm’s financial leverage
Globally, over the past two decades, female representation on corporate boards of directors has increased significantly in a number of markets At the same time, the issue of board gender diversity has also been debated and is the basis to consider the impact of female directors on a company's operations, including whether a greater presence of women on a BOD affects corporate financial decisions, and why, in fact, few women are on boards The pioneering research on this topic was conducted by Morrison, White, and Velsor (1987), and this topic has increasingly attracted the attention of many researchers globally, both in developed and developing countries
First, it has been shown that the maturity of a firm affects the composition of its board A high degree of board diversity is positively related to corporate financial results Looking at recent empirical studies, Tran, Hoang, and Tran (2015) investigate the impact of gender diversity in the BOD on company performance and find that the proportion of women directors on the board had a significant positive effect on the financial results of banks in ASEAN from 2009 to 2013 A BOD is more active in the presence of at least three female representatives Gender-balanced boards are also more likely to replace ineffective managers (Schwartz-Ziv, 2017) Rose et al (2013) and Marinova, Plantenga, and Remery (2015) studied the effects of women directors on the activities of companies in Germany and the Nordic bloc in 2010 However, their results show that the proportion of women on a BOD has no apparent influence on financial decisions In two other studies, Harris (2014) and Abobakr and Elgiziry (2015) find a significant negative relationship between the proportion of female directors and financial leverage, especially on boards where the presence of females accounts for 25% or more
2.1.4 CEO dual roles and a firm’s financial leverage
(7)decisions Duality exists when the CEO of a company is also the chairman of the board On the one hand, according to Sheikh and Wang (2012), duality provides clear direction from a single leader who can react more quickly to outside events On the other hand, duality increases the CEO's decision-making power by providing a broader base of power and strengthening control (Boyd, 1995) As a result, assigning both tasks to the CEO can weaken the board's control and influence financial decisions
Empirical research on this relationship gives mixed results Fosberg (2004) asserts that a dual leadership structure is effective in increasing the amount of debt in a firm's capital structure Abor (2007) finds a significant positive relationship between CEO duality and financial leverage A CEO often tries to finance the company's operations using debt capital instead of issuing new equity (Bokpin, & Arko, 2009) Meanwhile, Kyereboah-Coleman and Biekpe (2006) find a clear negative relationship between CEO duality and short-term and total leverage, asserting that agency costs increase when a CEO is chairman of the board, which discourages investors from investing in the business They also report a positive link between CEO duality and long-term leverage, but this relationship is not statistically significant Research by Tarus and Ayabei (2016) also confirms the negative relationship between CEO duality and financial leverage CEOs who are also chairs of the BOD are given too much power and have the ability to use less financial leverage to avoid risks associated with borrowing The study by Simpson and Gleason (1999) examines the effect of CEO duality on the use of financial leverage at 300 banks The results show that CEOs can influence the internal control system in a way that reduces the likelihood of financial difficulties for the company This means that they take less risk, resulting in underuse of financial leverage
2.2 Research in Vietnam
In Vietnam, recently published 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
(8)(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
(9)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
Table 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 if the CEO is also the BOD chairman, 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)
(10)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
(11)4 RESULTS AND DISCUSSION 4.1 Descriptive statistics
The authors collected data based on the variable definitions and methodology presented in Section The final dataset included 1,592 observations collected from 199 firms from 2012 to 2019 Summary statistics are presented in Table
Table 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 if the board has female members and if not; Ceodual is an indicator variable with value equal to if the chairman of the BOD also holds the position of CEO and 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
(12)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 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 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 Financial leverage by industries
(13)Table 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 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
(14)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 The results are shown in Table
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)
(15)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
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 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 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 show that L.Levit has a positive and
statistically significant correlation of 1% with Levit This means that the dynamic Model
(16)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 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)
(17)Table Regression results for static Models
Variable POLS_static (1) Random_static (2) Fixed_static (3) Fixed_static (4) Fixed_static (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
(18)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 if the board has female members, and if not Ceodual is an indicator variable equal to if the chairman of the BOD also holds the position of CEO, and 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 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 Regression results for dynamic Models
Variable POLS-dynamic (1)
Fixed-dynamic (2)
GMM-Dynamic (3)
GMM-Dynamic (4)
GMM-Dynamic (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]
(19)Table Regression results for dynamic Models (cont.)
Variable POLS-dynamic (1) Fixed-dynamic (2) GMM-Dynamic (3) GMM-Dynamic (4) GMM-Dynamic (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
(20)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 if the board has female members, and if not Ceodual is an indicator variable equal to if the chairman of the BOD also holds the position of CEO, and 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
(21)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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: http://dx.doi.org/10.37569/DalatUniversity.10.3.785(2020) CC BY-NC 4.0