Is the Concept of Corporate Governance a Strategic Plan for Firms’ Optimum Capital Structure? Evidence from Manufacturing Companies

Abstract

The aim of the article is to examine the relationship as well as measure the impact of corporate governance as a strategic plan on capital structure decision of top Bombay Stock Exchange-listed manufacturing firms in India.

Panel regression analysis is employed to estimate the relationship and measure the impact of corporate governance, namely, size, meetings, independent director, women director and audit committee meetings, on the capital structure mix (debt–equity ratio) of the sample corporate, during a 10-year period of 2008–2017.

The results of study reveal that the components of corporate governance, namely, size, board meetings, independent director, and audit committee meetings have a positive association with the capital structure variable (debt–equity ratio) of the sample manufacturing companies. However, there is a negative relationship between the control variables (ROCE and NWTA) and the dependent variable of the sample corporate. Overall, as per the study results, a statistically significant impact prevails on the capital structure, of corporate governance variables, taken as a whole.

This article adds on to the existing study by highlighting a new prospect of relation and influence of corporate governance on capital structure decisions. The statistical findings of the study provide evidence to the corporate sector in deciding the optimum capital structure, affecting its costs and performance, and to the regulatory authorities in framing and implementing corporate governance mechanisms more effectively and efficiently for improving the economy of the country.

Keywords

Corporate governance capital structure strategic plan operational plan meetings directors audit committee meetings debt–equity ratios

Introduction

Corporate houses are guided and regulated by rules, practices and processes known as corporate governance (CG) system. The CG system besides providing the framework for achieving corporate objectives covers every aspect of management also. CG is a system composed of a combination of board of directors (BoD) and its diverse composition for proper and profitable governance and management of an organization (Peizhi & Ramzan, 2020). It is considered as a great and useful tool in governance and management of corporate. In any corporate, the BoD is considered to be a major player of organization for safeguarding the interests of the stakeholders (Stapledon, 1997). Also, the BoD of an organization is the main stakeholder responsible for the decision-making process and for smooth formulation and implementation of all the strategic issues related to decisions and corporate strategies (Krechovska & Prochazkova, 2014).

‘In particular, the effective combination of well-diversified and well-organized boards as a component of corporate governance depends primarily on two factors, the first one is its structure, and the second is its defined roles’ (Cadbury, 1992). However, the future prospects of profit and growth of any corporate is decided by its structure of capital. The BoD is responsible for the best combination of equity and debt for optimum corporate capital structure (CS) creation (Bradley et al., 1984; Nyamweya, 2015). CG related to the BoD helps in the creation of proper balanced CS and hence supports the corporate in generating enhanced profit and its growth (Sheikh & Wang, 2012).

In any corporate house, it is the sole discretion of the BoD to decide upon the trade-off between the cost of financial distress and the interest tax shields which has to be carefully considered for an appropriate choice of CS (Kraus & Litzenberger, 1973; Peizhi & Ramzan, 2020). For better performance and growth of any organization, it is better to have a checked and relatively balanced CS or somewhat high equity financed, to avoid higher cost of debt and cash flows in terms of higher interest costs (Oyedokun et al., 2018). CG frames the rules and guidelines for an agent to operate, thereby safeguarding the interests of the principals, and provides a guidance on optimal composition of the BoD (Morellec et al., 2012). It also provides better understanding to the managers for optimum utilization of available resources to maximization of production levels. Thus, relatively balanced equity financing can be ensured with better CG, creating a check on the BoD and reducing agency costs (Mande et al., 2010). ‘Firms with bad governance practices face more agency problems as managers of those firms can easily gain private benefits due to poor corporate governance structure’ (Core et al., 1999). Therefore, good CG of an organization influences its growth and prosperity by influencing its CS decisions.

The financial goals of any corporate administration can be achieved and secured through the only possible way of having a structured and well-governed BoD which is protected against the failures in terms of the management of its finance and capital. Also, the achievement of corporate financial goals is dependent on the optimum utilization of its available resources, and hence this enhances the importance of CG in the field (Haat et al., 2010; Peizhi & Ramzan, 2020).

Rationale of the Study

CG is the fourth pillar of sustainability-reporting practices followed across the globe. CG is basically concerned with issues of management of the corporate houses to achieve the objectives of triple bottom line. Financial crisis, financial frauds, and other business frauds are the result of lack of governance. Therefore, CG has been made mandatory as per the regulatory requirements of corporates in order to ensure transparency and accountability in the day-to-day activities of the business. A good CG structure can be represented with a magnificent blend of the BoD and the diversity in its members. Again, optimum CS with appropriate debt and equity mix is one of the key decisions taken by the BoD for enhancing the profit and growth of an organization.

‘Corporate governance is concerned with the ways by which suppliers of capital assure themselves of getting returns on their investments’ (Shleifer & Vishny, 1997). The growth and development of an economy is characterized by the prevailing good CG practices. The countries with sound CG experience a healthy growth of their corporate sector and seem to be able to captivate more capital, enhancing the economy. Also, high-quality CG practices increase the corporate performance not only by improving its culture but also by reducing the cost of financing capital.

CG brings transparency and accountability in day-to-day business activities (Gupta & Singh, 2018). Besides investment decisions, one of the important business activities is financing the business operations, which is otherwise known as the business life blood. So, financing a business is done by the internal source or external source or a combination of both, known as CS. Effectiveness of the CS depends on its cost of raising finance (Nenu et al., 2018). If the cost of CS is minimized, then profitability and business growth are imperative, resulting in business sustainability. The principles of CG play a vital role in deciding the optimum CS (Kyriazopoulos, 2017). Therefore, our study to know the relation of CS and CG is relevant in the context of cut-throat competition in the global market.

Literature Review

Abor (2007) has done a study on ‘Corporate Governance and Financing Decisions of Ghanaian Listed Firms’. The researcher has examined the association between the CS and CG. For the study, the data were collected from 22 firms quoted in Ghana Stock Exchange over the period of 6 years, that is, 1998–2003. Debt ratio is considered as a CS variable and board size, board composition, CEO duality and CEO tenure are the measures of CG. The study has also taken size, ROA, risk and growth as the control variables. By employing multiple regression analysis, the study has revealed that the CS is influenced by the CG, except CEO tenure.

Chitiavi et al. (2013) have done a study on ‘Capital Structure and Corporate Governance Practices: Evidence from Listed Non-financial Firms on Nairobi Securities Exchange Kenya’. They wanted to study the influence on CS due to CG by taking 30 companies quoted in NSE, Kenya, for the period of 5 years. The study has used ownership concentration, board independence, institution share ratio, board size and CEO duality as the measures of CG and long-term debt–total asset ratio, debt–equity ratio, total debt–total asset ratio and short-term debt–total asset ratio as the proxies of CS. The study has employed the regression analysis and correlation matrix and concluded that total debt–total asset is positively influenced by the CEO duality.

Agyei and Owusu (2014) have conducted a study on ‘The Effect of Ownership Structure and Corporate Governance on Capital Structure of Ghanaian Listed Manufacturing Companies’. The study has used firm-level data collected from 8 companies out of 13 companies for the period of 5 years. The study has considered board composition, board size, CEO duality, institutional shareholding, board committees and shareholding of board members as the measures of CG. The leverage resembles debt/equity ratio and the study has also taken firm size and ROA as the control variables. By employing correlation matrix, descriptive statistics and multivariate regression analysis, the study has revealed that board size, board composition, and institutional shareholding are associated positively with the CS.

Bodaghi and Ahmadpour (2010) have done a study on ‘The Effect of Corporate Governance and Ownership Structure on Capital Structure of Iranian Listed Companies’. CG is measured by board size, CEO duality, and board composition. Ownership structure is measured by using institutional shareholding. CS is measured by debt/equity ratio and the study has taken profitability and firm size as the control variables. The study has concluded that board size is not influenced by the debt/equity ratio and financing decision is not influenced by the presence of non-executive directors and CEO duality. They have suggested that size and ownership structure are the key determinants of financial mix.

Saad (2010) has done a study on ‘Corporate Governance Compliance and the Effects to Capital Structure in Malaysia’ by using a sample of 126 companies for the period of 1998 to 2006. The study has used dual leadership, board meeting and board size which are the proxies of CG (explanatory variables). The study has considered interest coverage, debt ratio, and debt–equity ratio, which are the measures of CS (dependent variables). By using multiple regression analysis, the study has depicted that CG influence CS significantly.

Rehman et al. (2010) have done a study on ‘Does Corporate Governance Lead to a Change in the Capital Structure?’. They have examined the association of CS and CG by selecting 19 banks of Pakistan. CS is measured by long-term debts/capital employed ratio and the explanatory variables are board independence, ownership concentration, board size, managerial ownership, and number of meetings during one year. The study has applied multiple regression models and concluded that CS is not influenced by CG in banking sector of Pakistan.

Kajananthan (2012) has conducted a study on ‘Effect of Corporate Governance on Capital Structure: Case of the Srilankan Listed Manufacturing Companies’. The study has used 28 companies quoted in Colombo Stock Exchange over a period of 3 years. The explanatory variables are board meeting, board structure, size and proportion of independent directors whereas debt ratio is measured as dependent variable. By employing multiple regression analysis, the study has found that CG influences CS significantly.

Gill et al. (2012) have done a study on ‘Corporate Governance and Capital Structure of Small Business service Firms in India’. The study has collected data from 29 small business owners and considered board size, CEO duality and tenure, small business growth as the measures of CG. CS is taken as dependent variable and family is used as dummy variable. By employing descriptive statistics, factor analysis and regression model, the study revealed a positive influence of CG on CS.

A limited list of international empirical studies on the association between CS and CG is described below:

‘Thailand’s Corporate Financing and Governance Structures: Impact on Firm’s Competitiveness’. They have found a positive relationship between ownership concentration and leverage (Alba et al., 1998).

‘Corporate Governance and Capital Structure Decisions of the Chinese Listed Firms’. The study has found a negative association between board composition and CEO tenure with leverage (Wen et al., 2002).

‘Ultimate Corporate Ownership Structures and Capital Structures: Evidence from East Asian Economies’. It is found that controlling owners prefer higher debt (Du & Dai, 2005).

‘The Determinants of Capital Structure: Capital Market Oriented versus Bank Oriented Institutions’. They found that CG influences the CS of a firm (Antoniou et al., 2008).

‘Effect of Corporate Governance on Capital Structure: Case of the Iranian Listed Firms’. The study results show that there is a negative association between leverage and board size, and a positive association between CEO duality and leverage (Vakilifard et al., 2011).

‘Impact of Corporate Governance and Capital Structure on Firm Financial Performance: Evidence from Listed Cement Sector of Pakistan’. They have concluded that audit committee and board size have insignificant association with the firm financial performance (Jamal & Mahmood, 2018).

‘Managerial Entrenchment and Capital Structure Decisions’. They have found that large boards put more pressure on the managers (Berger et al., 1997).

Kumar (2015) has done a study on ‘Capital Structure and Corporate Governance’ by using 5,117 firm-level data of 2,517 manufacturing firms quoted in Bombay Stock Exchange (BSE) from 1994 to 2000. The study has included ownership as the explanatory variable which is measured by managerial shareholding, foreign investors’ shareholding, institutional investors’ shareholding, corporate shareholding and CS is taken as the dependent variable. The study has also taken age, tangibility, size, marketing, distribution, advertising, profitability, and R&D expenses as the control variables. By employing descriptive statistics and regression analysis, the study has concluded that CS is not associated with the CG.

Chadha (2015) have done a study on ‘Impact of Governance on Capital Structure: Evidence from Bombay Stock Exchange Listed companies’. The study has considered board size, ownership concentration, outside directors as the measures of CG and short-term debt/equity ratio, long-term debt/equity ratio are considered as the dependent variables. The study has employed correlation matrix, panel regression analysis and revealed a positive influence of CG on CS.

Research Gap

The relationship between CG and CS is imperative on the basis of the role it plays in generation and distribution of value (Bhagat & Jefferis, 2002). The CS is capable of creating value through the interaction of CG instruments. The CG as business strategy shows the way of distributing values among the stakeholders (Zingales, 1998). In other words, CG is an integral part of value creation in the business.

The aforementioned literature on CS and CG has given mixed results. Chitiavi et al. (2013) analyzed the impact of CG on CS by taking 30 companies quoted in NSE, Kenya, for a period of 5 years and found a positive relationship. Similar positive relationship was also reported by Abor (2007), Agyei and Owusu (2014), Alba et al. (1998), Antoniou et al. (2008), Chadha (2015), Gill et al. (2012), Kajananthan (2012), and Saad (2010). Some studies such as Bodaghi and Ahmadpour (2010), Jamal and Mahmood (2018), Kumar (2015), Rehman et al. (2010), Vakilifard et al. (2011), and Wen et al. (2002) focused on the effect of CG on CS and revealed no influence of governance on CS. It is learnt from the literature that many studies have been undertaken in the context of foreign countries. There are a few studies on the relationship of CG and CS in the Indian context. Due to such mixed results, there is a need for an Indian-specific study in order to conclude which school of thought is supported by the companies in India.

Research Question

Abor (2007) examined the association between the CS and CG and identified that CS is positively influenced by the CG except CEO tenure. Chitiavi et al. (2013) revealed that there is a positive association between total debt/total asset and the duality of the CEO. In addition, according to Wen et al. (2002), there exists a positive association between the structure of capital and the size of the board. He argued that companies having large BoD follow a high level of gearing which is aimed at increasing the value of the company. On the other hand, Berger et al. (1997) argued that companies with large BoD have a low level of gearing. Berger et al. (1997) also found that a large board exerts more pressure on the managers since they are required to enhance the firm’s performance while maintaining lower level of gearing.

Bodaghi and Ahmadpour (2010) identified a negative association between board size and the debt/equity ratio and financing decision is not influenced by the CEO duality and the presence of non-executive directors on the board. Rehman et al. (2010) depicted that CS is not influenced by the CG in the banking sector of Pakistan. Wen et al. (2002) provided evidence that there exists a negative association between the representation of non-executive directors and the gearing level. The main reason for the negative relationship is that the non-executive directors are capable of monitoring the managers; therefore, managers are forced to adopt lower debt.

In India, previous studies have examined CS determinants and CG issues. However, the findings are mixed as evidenced by Kumar (2015) who used 5,117 firm-level data of 2,517 manufacturing firms quoted in BSE from 1994 to 2000 to investigate the effect of CG on CS decisions. He identified that CS is not associated with the CG. Chadha (2015) set out to analyze the effect of governance on CS of the firm that is quoted in BSE using board size, ownership concentration, outside directors as variables concluded that CS is positively influenced by the CG.

On the basis of research gap and problem as found by the above studies, the researchers aimed to address the following questions:

Is there any significant relationship between components of CG and CS?

Do components of CG as a business strategy help in optimization of CS?

Purpose of Study

Based on research questions, we have designed the following objectives:

To establish the relationship between components of CG and CS

To measure the impact of CG as a business strategy on optimization of CS

Research Methodology

Data

This study analyzes the impact of CG on CS of manufacturing companies listed in the BSE during 2008–2017. Our study is based on purposive and convenient sampling where data were collected from the annual reports of companies. Purposively, we have taken top 100 BSE-listed companies on the basis of market capitalization. Out of the top 100 BSE-listed companies, 56 manufacturing companies were found and others were non-manufacturing companies. Due to missing of data in 11 manufacturing companies, finally we took 45 manufacturing companies over a period of 10 years for our study.

Variables

The study has used CS as the dependent variable which is measured by total debt ratio, that is, debt to equity ratio. The explanatory variables include board size, board meetings, audit committee meetings, independent directors, and women director. In addition to the above CG variables, the study has also included some control variables in the model which may be affecting the CS decision. Control variables include the firm’s value, firm’s profitability, and financial position of the company. Measurements of all these variables are discussed below. Further, measurement of all variables is largely adopted from existing literature.

Measurement

Based on previous literatures in accounting, the measures were taken. They are as follows:

Board size, board meetings, and CS were adopted from Saad (2010)

Audit committee meeting were adopted from Bansal and Ahmadpour (2010)

Independent directors were adopted from Kajananthan (2012)

Measures pertaining to women director were adopted from Harris (2014)

Firm’s profitability (ROCE) and financial position (long-term debt to total assets) were adopted from Bodaghi and Ahmadpour (2010)

Firm’s value (net worth to total assets) was adopted from Siddik et al. (2017)

Description of Variables

Board Size (independent variable) was calculated by computing as a percentage of members in the board to total statutory requirement of board size, that is, 15.

Board Meeting (independent variable) was measured by calculating the percentage of total number of meetings organized during the year to statutory requirement with regards to board meetings, that is, 4 per year.

Audit Committee Meeting (independent variable) was measured by taking a percentage of audit committee meetings held during a year to number of meetings to be held as per regulatory framework, that is, 4 per year.

Independent Director (independent variable) was measured by computing a percentage of number of independent directors in the board with respect to the board size of the company.

Women Director (independent variable) was measured by computing a percentage of number of women directors in the board with respect to the board size of the company.

ROCE (Return on Capital Employed), as the control variable, was measured by computing total earnings after tax to capital employed by the company. It was the proxy of firm’s profitability.

NWTA (Net Worth to Total Assets) (control variable) was measured by computing the ratio of net worth to total assets of the company. It represents the firm’s value.

LDTA (Long-term Debt to Total Assets) (control variable) was measured by computing the ratio of long-term debt to total assets of the company. It shows the financial position of a firm.

Debt to Equity Ratio (Capital Structure; dependent variable) was measured by computing the ratio of total debts of a company to the total equity of the company at balance sheet date. It is a proxy of CS.

The Model

The study applies a panel data methodology. Panel data involve cross section data over several time periods and gives results that are not detectable in pure cross section or pure time series data (Abor, 2007). The model for this study follows the model already used by Chadha (2015).

The model is:

\begin{array}{l} {DR}_{i, t} = β_{0} + β_{1} {BS}_{i, t} + β_{2} {BM}_{i, t} + β_{3} {ID}_{i, t} + β_{4} {WD}_{i, t} + β_{5} {ACM}_{i, t} + β_{6} {ROCE}_{i, t} + β_{7} \\ {NWTA}_{i, t} + β_{8} {LDTA}_{i, t} + e \end{array}

where β₀ is a constant; β₁, β₂, β₃, β₄, β₅, β₆, β₇, β₈ are the slopes; DR_i,t (total debt ratio) is the total debt per total equity for firm i in time t; BS_i,t (board size) is the percentage of members in the board to total statutory requirement of board size for firm i in time t; BM_i,t (board meeting) is the percentage of total number of meetings organized during the year to statutory requirement with regards to board meetings for firm i in time t; ID_i,t (independent director) is the percentage of number of independent directors in the board with respect to the board size of the company for firm i in time t; WD_i,t (women director) is the percentage of number of women directors in the board with respect to the board size of the company for firm i in time t; ACM_i,t (audit committee meeting) is the percentage of audit committee meetings held during a year to number of meetings to be held as per regulatory framework for firm i in time t; ROCE_i,t (return on capital employed) is the total earnings after tax to capital employed for firm i in time t; NWTA_i,t (net worth to total assets) is the net worth to total assets for firm i in time t; LDTA_i,t (long-term debt to total assets) is the long-term debt to total assets for firm i in time t; and e is the error term.

Statistical Tools and Techniques

We have developed panel data with three models, that is, Pooled-OLS Model, Fixed-Effect Model, and Random-Effect Model.

The impact of five explanatory variables, that is, BS, BM, ID, WD, and ACM on the CS (D/E ratio) along with three control variables as ROCE, NWTA, and LDTA is analyzed using panel data analysis statistical tool.

Hausman test: It is applied to find out the best fit model for the analysis study from among the fixed-effect and random-effect models.

Unit Root (Augmented Dickey Fuller/ADF) test: It is used to determine the normality of data.

Multicollinearity test: It is applied to find out the interrelationship among the independent variables and correlation matrix analysis have also been used for the study.

Software Used for Data Processing

SPSS and E-Views have been used for estimation and processing of data.

Null Hypothesis

H₀¹: CG does not impact CS of sample manufacturing companies of the study.

Data Analysis and Findings

Table 1.

Pearson Correlation Analysis

Pearson Correlation Analysis
	D/E	ACM	BM	BS	ID	WD	ROCE	NWTA	LDTA
D/E	1	0.068	0.045	0.016	0.010	–0.088	–0.271	–0.245	0.484
ACM		1	0.493	0.462	0.277	0.047	–0.114	–0.078	0.046
BM			1	0.412	0.288	0.064	0.013	–0.115	0.080
BS				1	0.284	0.049	–0.166	–0.081	0.089
ID					1	–0.075	–0.051	–0.075	0.043
WD						1	0.193	0.060	–0.069
ROCE							1	0.098	–0.389
NWTA								1	–0.280
LDTA									1

Source: Self-compiled.

As evidenced from the above Table-1, BS, BM, ACM, and ID are positively associated with debt to equity ratio (CS). It means, more the members in the board, board meetings, audit committee meetings and independent directors, more will be the debt to equity ratio (CS) of the companies. It may be because of the reason that increased BS, BM and ACM, results in better transparency and control, and thereby enhances credit-worthiness attracting more lenders and making debt capital available at a lower cost, which in turn increases debt component in the firm’s CS. CG and CS are positively correlated which is consistent with the results of Abor (2007) and Rajendran (2012). However, as per the results, WD is negatively associated with the debt to equity ratio (CS). This can be justified by the reason that presence of women directors in the board may lead to a conservative decision of the board thereby avoiding the risk of debt capital in the CS of the companies. In other words, women may be risk averse in nature.

Also, the control variables, ROCE and NWTA, are found to be negatively associated with debt to equity ratio. It may be based on the cause and effect relationship that, more and more debt capital used by a firm more will be the finance cost to be borne by it, and also with more debt capital infusion, the risk of shareholders will be enhanced, demanding for a greater return for more risk, which as a result reduces the profitability and hence, reduced net worth of the companies. However, the LDTA is positively associated to debt to equity ratio as more the long-term debt of a company more will be the debt to equity ratio. From the correlation table it is evident that the correlation among the independent variable is below the statutory norm, that is, 0.80 (Kumari, 2008). It shows that there exists no multicollinearity among the independent variable. To substantiate this result, we have also used variance inflation factor (VIF) under multicollinearity test in Table 2.

Multicollinearity Test

From the above report it is found that VIF of the explanatory variables is less than the thumb rule, that is, 10 (Besley et al., 1980). So, it can be said that the explanatory variables do not have multicollinearity and it is inferred that all the variables are eligible for panel regression test.

Table 2.

Multicollinearity Test

Model	Collinearity Statistics
Model	Tolerance	VIF
Constant
BS	0.725	1.379
BM	0.697	1.434
ID	0.872	1.146
WD	0.992	1.008
ACM	0.668	1.498

Source: Self-compiled.

ADF Stationary Test

Table 3.

Stationary Test Result

Models	Probability Value	Decision
ADF test	0.0000^a	Stationary

Source: Self-compiled.

Note: ^a represents significance at 5 percent.

On applying the ADF test, the result from Table 3 shows that the significance of all variables under study is 0.00 which is within the accepted norm of 0.05. So, it satisfies the alternative hypothesis that the series has no unit root, that is, data is stationary. Hence, further analysis with the help of panel data models can be done for the study of objective.

Hausman Test

Table 4.

Hausman Test Result

Models	Probability Value	Decision
Hausman Test	0.0140^a	Fixed-effect model

Source: Self-compiled.

Note: ^a represents significance at 5 percent.

H₀ = Random effect is appropriate.

On applying Hausman test to the panel data, as per the above table-4, the probability value comes to be 0.0140 which is less than the acceptable benchmark of 0.05 (Wooldridge, 2013). So, we cannot accept the null hypothesis. Hence, the alternative hypothesis that fixed-effect model is appropriate is accepted.

Fixed-effect Model Analysis

Table 5.

Regression Results for Fixed-effect Method Estimation (Dependent Variable = D/E Ratio)

Models	R ²	Adjusted R²
Model	0.439575	0.366169
Variables	Dependent Variable: Debt to Equity Ratio
Variables	Coefficient	Probability
Constant	0.263205	0.3515
Independent variables
BS	–0.014933	0.9767
BM	–0.060929	0.6471
ACM	0.081393	0.6281
ID	0.414384	0.5160
WD	0.241661	0.7380
Control variables
ROCE	–0.004916	0.1504
LDTA	0.020009	0.0000
NWTA	–0.130953	0.2623

Source: Self-compiled.

Table 5 shows that R² is 43.96 percent. It means, keeping other factors constant, debt to equity ratio (CS) is determined to the extent of 43.96 percent by the five explanatory variables of CG and three control variables taken for our study.

\begin{array}{l} D/E ratio = 0.26 - 0.01 BS - 0.06 BM + 0.08 ACM + 0.41 ID + 0.24 WD - 0.01 ROCE + \\ 0.02 LDTA - 0.13 NWTA \end{array}

Here, the value of β₀ is 0.26. It means the company would have a positive debt to equity ratio of 0.26 even in the absence of any other factor. The slope measures the impact of independent variables of CG towards debt to equity ratio. It infers that a unit percentage change in the explanatory variables causes the respective percentage change in the debt to equity ratio of sample manufacturing companies. For BS, the slope is −1 percent. This result supports the argument that larger board practice pressure on managers so that they adopt lower debt levels to avoid this pressure, which is consistent with Anderson et al. (2004), Butt and Hasan (2009), and Heng et al. (2012). The empirical results of this study contradict with the evidences of Sheikha and Wang (2011) and Hussainey and Aljifri (2012).

The coefficient for BM is −6 percent and that of ACM is 8 percent. The number of audit committee meetings held is found to have a positive relationship with the dependent variable, that is, increase in the audit committee meetings increases the debt proportion in capital. This may be due to the enhanced faith of the lenders due to increased meetings, resulting in investment by lenders at a lower cost. This result is also supported by the study of Bansal and Sharma (2016) which shows a negative relation of ACM with ROCE which means reducing the equity proportion for more debt. On the other hand, the result contradicts with the evidence of Anderson et al. (2004) and Klien (2002).

Again, the relative percentage change in ID is 41 percent and WD is 24 percent. Abor (2007), Berger et al. (1997), and Jensen (1986) also argue that there is a positive correlation between the proportion of independent directors and debt ratio. Executive directors prefer to have low level of debt in contrast with external directors who prefer high leverage to enhance the performance of the firm (Omar, 2017). Kuo et al. (2012) and Wen et al. (2002) evidenced that there is a negative correlation between debt and independent director.

The positive relationship of women directors in board and the debt to equity ratio is further supported by Harris (2014) whose study result reflects that given average board age, when there is greater gender diversity on the board, it is associated with a higher debt ratio. The result is also supported by Adams and Ferreira (2009) and Kang et al. (2010).

The coefficients of control variables as return on capital employed and net worth to total assets are statistically insignificant and negatively related to the debt to equity ratio. The coefficient of long-term debt to total assets is statistically significant and positively related with the debt to equity ratio.

Also, the probability value (F statistics) of the result is calculated to be 0.00 which is within the limit of acceptance, that is, 0.05. Hence, our Null Hypothesis, that there is no significant impact of CG on CS of sample companies, is not accepted. In other words, it shows that the variables of CG taken for the purpose of our study, as a whole, have a statistically significant impact on CS of the sample manufacturing companies. This result further evidences the findings of Graham and Harvey (2001) and Litov (2005, pp. 1–43) that there is a correlation of CG with the financing decisions and thus firms’ CS.

Findings

The overall findings of the study as discussed above can be summarized as:

Components of CG namely, board size, board meetings, independent directors, and audit committee meetings are positively related to the CS variable (debt to equity ratio) of the sample companies.

A contrasting relationship is found between the control variables, ROCE and NWTA, and CS variable of the sample companies.

The CG variables, taken as a whole, have a statistically significant impact on the CS of the companies.

Suggestions

The variables of CG have a significant impact on CS decisions of the companies. Such policies and decisions with regard to CG and CS need to be implemented and executed by the top levels of management. Hence, the top executive decision makers need to have integrity, honesty, ethical attitude, commitment and passion for their job for effective and efficient CG and CS policies. So, if these elements are inculcated and such supportive working environment is created for the top executives to work in, then the main purpose of CG can be better achieved.

Conclusions

This article examines the relationship and impact of CG on the CS of the top manufacturing companies listed with BSE for the period 2007–2017 using panel regression analysis and other statistical tools and techniques. The results of this research study show that board size, board meetings, independent directors, and audit committee meetings are positively related to the debt to equity ratio of the sample companies, with implication that more the board size, number of board meetings, presence of independent directors, and number of audit committee meetings, more will be the debt capital of the companies. This may be due to the better transparency and control of companies by enhanced board size, board meetings, independent directors, and audit committee meetings, resulting in better confidence of lenders and debt availability at cheaper cost. However, a contrasting relationship is found between the control variables, ROCE and NWTA, and CS variable of the sample companies. This negative relationship can be substantiated by the fact that more debt infusion in the CS of the companies leads to more finance costs and as a result less profitability and net worth. Also, the results reveal that the CG variables, taken as a whole, have a statistically significant impact on the CS of the companies.

The issue of CG should therefore be taken more seriously rather than just as a regulatory compliance. Having properly understood the importance of CG, firms should implement it as a protective measure for its stakeholders and for the overall interest of the society.

Practical Implications of the Study

This article contributes to the existing literature study by providing a new prospect of the relationship and impact of CG practices on CS decisions. The practical implication of the study is that the corporate sector of a country contributes significantly to the growth and prosperity of the economy of a nation. The growth, development, and sustainability of corporate sector are largely influenced by their governance practices. CS decision is one of the three financial decisions of a company which determines the profitability and growth of the business. The optimum CS decision is dependent on the proper CG practices adopted by companies. Therefore, measurement of impact of CG on CS will add value to the decisions taken by the corporate with respect to the optimum CS.

It is the sole responsibility of the BoD of corporate, in this case, to decide upon the financing capital mix based on the current and future prospects of market conditions. The study provides evidence that CG practices affect the board composition of corporate and thus affect the decision of managers with regards to the optimum CS, and it directly affects the corporate performance and thus economy of nation. BoD should focus on reducing the cost of financing by reducing agency costs to improvise the corporate performance and this can be possible by implementation of sound CG structure in the country.

Limitations of the Study

One of the major limitations of the study is the unavailability of data of some companies listed in Bombay Stock Exchange. The time period for our study is 10 years based on convenient and purposive sampling. So, the time period could be extended to have better insight into the study. Another limitation is that our study is based on secondary data which suffers from its inherent limitations.

Scope for Future Study

The above related study and research can be further taken up by other researchers by extending the scope of research to other non-manufacturing companies of the country and the foreign companies. Also, various other determinants and variables of CG and CS policies can be considered to study their different dimensions and perspectives.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Himanshu Agarwall

Chandrika Prasad Das

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