Impact of Corporate Governance on Credit Ratings: An Empirical Study in the Indian Context

Abstract

Credit rating is the judgement of a credit rating firm of the creditworthiness of an entity as well as its ability to repay outstanding debt. Prior literature on credit ratings has majorly identified firm-specific characteristics as well as the characteristics of the debt issued as the primary factors affecting credit ratings. However, effective governance mechanisms can affect the credit ratings of a firm by way of their influence on a firm’s default risk. The present article is an attempt to discern the relationship between corporate governance and credit ratings by studying the Bombay Stock Exchange listed Indian firms that received a credit rating from CRISIL for their long-term debt during any of the 5 years from 2013–2014 to 2017–2018. It would add to the existing literature by assessing the association between corporate governance mechanisms and credit ratings in the Indian context since all other studies relate majorly to the Western parts of the world.

Keywords

Corporate governance credit ratings ordinal logistic regression binary logistic regression investment grade speculative grade

Introduction

Credit rating is the judgement of a credit rating firm of the creditworthiness of an entity as well as its ability to repay outstanding debt (Ashbaugh-Skaife et al., 2006). Prior literature on credit ratings has majorly identified firm-specific characteristics as well as the characteristics of the debt issued as the primary factors affecting credit ratings (Kaplan & Urwitz, 1979; Weinstein, 1981; West, 1970). However, the possibility of default of a firm would also be dependent on the ‘information risk’, that is, the risk that the managers of a firm may be in possession of certain information that might have a negative implication on the risk of default of a firm, as well as the ‘agency risk’, that is, the risk that relates to the self-interested behaviour of the management in which they take actions that do not maximise the value of the firm (Bhojraj & Sengupta, 2003). Therefore, the governance mechanisms within a firm can affect the evaluation of the likelihood of default of a firm.

Weak governance can severely affect the financial position of a firm, thus leaving the debtholders exposed to the risk of a loss (Ashbaugh-Skaife et al., 2006). On the contrary, good governance incorporates certain mechanisms that help to minimise agency conflicts by way of providing incentives to make and implement good decisions as well as by way of discouraging the choices that damage the value of a firm (Aman & Nguyen, 2013). Thus, good governance helps instil a sense of confidence among the holders of debt that the company would never take decisions affecting them in a negative manner.

Previous studies on the association between the corporate governance systems and credit ratings in case of the US firms maintain that good governance practices are valued highly by the credit market participants. Bhojraj and Sengupta (2003) found that board independence and effective monitoring tend to have a significant positive association with credit ratings. Ashbaugh-Skaife et al. (2006) confirmed that after incorporating controls for a number of unsystematic risk factors, credit ratings had a negative association with the number of blockholders as well as CEO duality, and a positive relationship with the independence, expertise and stock ownership of the board. In case of the Japanese firms, Aman and Nguyen (2013) found higher credit ratings to be a feature of institutions associated with good practices of corporate governance, with the level of institutional ownership, board size as well as disclosure quality being positively associated with credit ratings.

The present article is an attempt to discern the relationship between corporate governance and credit ratings by studying the Bombay Stock Exchange (BSE) listed Indian firms that received a credit rating from CRISIL for their long-term debt during any of the 5 years from 2013–2014 to 2017–2018. It would add to the existing literature by assessing the association between corporate governance mechanisms and credit ratings in the Indian context since all other studies relate majorly to the Western parts of the world. Also, the study takes into account a wide variety of governance characteristics as compared to just a few that have been captured in prior research.

Literature Review and Hypotheses

There are two strands of literature relating to the relationship between corporate governance and credit ratings. The first one portrays the credit rating agencies as an important gatekeeper of the financial market and explains that they play a crucial role in corporate governance (Coffee, 2006; Partnoy, 2006; Roychowdhury & Srinivasan, 2019). As gatekeepers, rating agencies have a fiduciary responsibility towards the investors and help prevent any financial wrongdoings in the capital market (Coffee, 2006; Roychowdhury & Srinivasan, 2019). Further, according to Coffee (2006, p. 8), ‘Effective corporate governance requires a chain of actors: directors, managers, and gatekeepers’. The board can perform the governance function efficiently only if the gatekeepers like the rating agencies, properly advise and warn the board (Coffee, 2006). Hence, credit ratings can be regarded as an important governance tool. The other strand of literature highlights that corporate governance is an important input to the credit ratings granted by rating agencies. Empirical literature on the relationship between corporate governance and credit ratings focuses primarily on this strand (e.g., Alali et al., 2012; Ashbaugh-Skaife et al., 2006; Bhojraj & Sengupta, 2003).

Good governance structures are expected to minimise the risk to the bond holders of a firm since they help lower the agency costs of that firm which eventually leads to higher credit ratings (Aman & Nguyen, 2013). Previous studies on the relationship between corporate governance and credit ratings have primarily focused on the characteristics of the board of directors, audit committee as well as the ownership pattern of a firm (Alali et al., 2012; Aman & Nguyen, 2013; Ashbaugh-Skaife al., 2006; Bhojraj & Sengupta, 2003; Sengupta, 1998).

Board Size is an essential feature of the board that has an impact on the potency of the board in supervising the decisions and actions of the management. Some studies report that larger boards may lead to lack of coordination amongst the board members and firms with such boards tend to have lower market values (Lipton & Lorsch, 1992; Yermack, 1996). Kumar and Singh (2013) found a negative relationship between board size and firm value in case of 176 Indian companies listed on the BSE 200 Index. However, there are studies to the contrary that have found that larger boards can lead to an increase in the effectiveness of the monitoring function because of greater task sharing amongst the members (Lehn et al., 2009). Dwivedi and Jain (2005) found a positive association of board size and firm value in the Indian context. Also, Mishra and Kapil (2017) reported a positive impact of board size on the accounting-based performance in Indian companies. Moreover, larger boards should be seen more favourably by the debt holders since decision-making groups that are large have a general inclination to assume less risk (Adams & Ferreira, 2010; Cheng, 2008). Also, Aman and Nguyen (2013) found that credit ratings increased with the board size. Hence, the hypothesis has been framed as follows:

H_1a: Board size has a significant positive impact on credit ratings.

Independent or outside directors generally give more impartial opinions and hence are considered to be better at monitoring the management as compared to the insiders (Guo & Masulis, 2015; Hermalin & Weisbach, 1998). Also, independent directors help protect the interest of the debtholders by way of enhancing the performance of the firm (Aman & Nguyen, 2013). Jackling and Johl (2009) found a positive association between the proportion of outside directors and firm performance in case of top Indian companies. Chakrabarti et al. (2010) suggested the independent directors, owing to their monitoring role, add value in case of Indian firms. Further, Bhojraj and Sengupta (2003) found that credit ratings increased with the proportion of independent directors on the board. Hence, the hypothesis has been framed as follows:

H_1b: Board independence has a significant positive impact on credit ratings.

The board activity or the number of meetings of the board is another essential characteristic of the monitoring effort of the directors (Vafeas, 1999) Companies where the boards tend to meet frequently show improved financial performance as compared to the companies where boards do not do so (Vafeas, 1999). Arora and Sharma (2016) reported a positive impact of the number of board meetings on the firm value in case of Indian companies. Lorca et al. (2011) found that increased board activity helps reduce information asymmetry and agency costs, thus influencing the debtholders’ risk assessment. Hence, the following hypothesis has been framed:

H_1c: Number of board meetings has a significant positive impact on credit ratings.

In case of firms with CEO duality, that is, where the CEO is also the chairman of the board, the monitoring function is severely affected since there tends to be a greater alignment of that individual with the management than with other stakeholders of the firm (Fama & Jensen 1983; Goyal & Park, 2002; Jensen, 1993). Consistent with this argument, Ashbaugh-Skaife et al. (2006) found that CEO duality negatively affects the credit rating of a firm. In case of Indian firms, Shrivastav and Kalsie (2016) reported a negative impact of CEO duality on the firm performance. Hence, the hypothesis has been framed as follows:

H_1d: CEO duality has a significant negative impact on credit ratings.

The audit committee also has a crucial function that is related to the monitoring and oversight of the accounting procedures with an aim of authentic and reliable reporting to the stakeholders of a firm (Beasley, 1996; Pincus et al., 1989). The number of members in an audit committee may impact the effectiveness of its processes. Larger audit committees generally include people having expertise in varied areas to carry out an intense monitoring and oversight function. Also, firms having large audit committees generally implies that they are inclined towards devoting more resources for overseeing the accounting processes (Pincus et al., 1989). Anderson et al. (2004) found that audit committees that are large in size are viewed by the debtholders in a positive way since they provide for greater accounting transparency. Also, Aman and Nguyen (2013) found that such accounting transparency further affects the credit ratings in a positive manner. Mishra and Malhotra (2016) reported a positive association between the size of audit committee and the earnings quality in case of Indian firms. Hence, the following hypothesis has been framed:

H_1e: Audit committee size has a significant positive impact on credit ratings.

It is said that the audit committees having a higher number or ratio of independent directors can more effectively carry out their functions (Klein, 2002). Also, since better oversight of the reporting processes increases the financial transparency and reduces managerial opportunistic behaviour, it should also lead to lowering the default risk for the debtholder (Ashbaugh-Skaife et al., 2006). Audit committees which are fully independent are viewed positively by the debtholders (Anderson et al., 2004). In the Indian context, Bansal and Sharma (2019) reported a positive relationship between audit committee independence and firm performance. Hence, the following hypothesis has been framed:

H_1f: Audit committee independence has a significant positive impact on credit ratings.

Audit committees that are inactive or do not meet often are unlikely to be able to monitor and oversee the management effectively. Effective audit committees are the ones that meet on a regular basis (Menon & Williams, 1994; Xie et al., 2003). Anderson et al. (2004) found that audit committee activity is a crucial feature of governance as viewed by the debtholders. Bansal and Sharma (2019) found a positive impact of the frequency of audit committee meetings and firm performance in case of Indian companies. Hence, the hypothesis has been framed as follows:

H_1g: Number of audit committee meetings has a significant positive impact on credit ratings.

Block holders play a crucial part in the functioning of the system of governance since they tend to possess an interest in the firm that is financial in nature. They can pressurise the management for a corrective action whenever required since they also possess the necessary voting power (Gordon & Pound, 1993; Jensen, 1993; Shleifer & Vishny, 1997). Nashier and Gupta (2020) reported a positive impact of ownership concentration on firm performance in the Indian context. On the other hand, it has been found that ownership concentration allows such shareholders to secure certain benefits by imposing inordinate pressure on the management which might adversely affect the minority shareholders as well as the bondholders (Shleifer & Vishny, 1997). Studies particularly pertaining to the linkages between corporate governance and credit ratings have found that credit ratings tend to increase with the ownership concentration (Aman & Nguyen, 2013; Ashbaugh-Skaife et al., 2006; Bhojraj & Sengupta, 2003). Hence, the hypothesis has been framed as follows:

H_1h: Ownership concentration has a significant positive impact on credit ratings.

Another important variable is the type of ownership of a firm since the identity of the main shareholder may also affect the credit ratings. Studies have documented that companies that are characterised by large institutional ownership tend to receive higher credit ratings (Aman & Nguyen, 2013; Ashbaugh-Skaife et al., 2006; Bhojraj & Sengupta, 2003). In case of India, promoters generally have the largest equity stakes in companies (Jaiswall & Bhattacharyya, 2016). Also, promoter ownership has been shown to have a positive relationship with firm performance in Indian firms (Kumar & Singh, 2013; Mishra & Kapil, 2017). Hence, the hypothesis has been framed as follows:

H_1i: Ownership type has a significant impact on credit ratings.

Data and Methodology

Sample Selection and Data Sources

A subset of the companies that received a credit rating from CRISIL for their long-term debt in any of the 5 years from 2013–2014 to 2017–2018 constituted the sample for the present study. From the companies that did receive a credit rating, first of all, companies other than public limited companies were removed. Second, the companies that did not issue long term debt during the said period were excluded since the study tended to focus on the credit ratings received by the companies on the long-term debt issued by them. Third, the companies not listed on the BSE were removed. Fourth, all banking and other financial companies, public-sector companies (both at the central and state level) and the companies having financial year ending other than on 31 March, were also deleted for consistency. Finally, the companies for which the data required was missing were also removed which resulted in a final sample of 155 companies as shown in Table 1.

The data sources for the present study included the CMIE Prowess database as well as the annual reports of the companies.

Table 1.

Summary of Sample Selection Criteria

Selection Criteria		No. of Companies
Companies that received a credit rating from CRISIL in any of the 5 years from 2013–2014 to 2017–2018		2,689
Less:
	Companies other than public limited cos.	(1,273)
	Companies that did not issue long term debt during the period	(819)
	Companies not listed on BSE	(407)
	Banking and other financial companies	(13)
	Public-sector companies (Central and State)	(10)
	Companies with accounting year ending other than on 31 March	(1)
	Companies for which data could not be obtained	(11)
	Usable Sample	155

Source: Author’s own calculations.

Variable Selection and Description

Credit Rating

For Credit Rating (CR), the dependent variable, the rating given by CRISIL, the largest credit rating agency in India, to the long-term debt issued by the sample companies was used. These indicate CRISIL’s evaluation of the creditworthiness of the borrower company. The CRISIL rating scale for long term debt instruments starts from AAA (highest safety) and goes all the way down to D (default). Following prior studies, these ratings were segregated into seven categories for the purpose of analysis as shown in Table 2 (Alali et al., 2012; Ashbaugh-Skaife et al., 2006; Elbannan, 2009). Also, another classification scheme was followed that categorised those ratings into two distinct grades namely investment and speculative grade measured by the variable ‘INV_GRADE’. This variable was coded as ‘1’ if the credit rating received by the firm was BBB or higher, and ‘0’ otherwise (Alali et al., 2012; Elbannan, 2009).

Table 2.

Credit Rating Classifications

CRISIL Debt Rating	Assigned Score(CR)	Grade	Assigned Score(INV_GRADE)
AAA	7	Investment	1
AA+	6	Investment	1
AA	6	Investment	1
AA−	6	Investment	1
A+	5	Investment	1
A	5	Investment	1
A−	5	Investment	1
BBB+	4	Investment	1
BBB	4	Investment	1
BBB−	4	Investment	1
BB+	3	Speculative	0
BB	3	Speculative	0
BB−	3	Speculative	0
B+	2	Speculative	0
B	2	Speculative	0
B−	2	Speculative	0
C	1	Speculative	0
D	1	Speculative	0

Note: Credit Rating denotes the rating given by CRISIL to the long-term debt issued by the sample companies. These ratings range from AAA to D for the sample companies. For the purpose of analysis, these ratings have been classified into seven categories as shown in the table above. Also, ratings below BBB have been classified as ‘Speculative’ as per S&P ratings classification.

Source: Drawn from literature.

Corporate Governance Variables

Studies on the relationship between corporate governance mechanisms and credit ratings have primarily focused on the board characteristics, characteristics of the audit committee as well as the ownership pattern of a firm, as proxy measures of corporate governance (Alali et al., 2012; Aman & Nguyen, 2013; Ashbaugh-Skaife et al., 2006; Bhojraj & Sengupta, 2003; Sengupta, 1998). Hence, the governance variables used in the study include CEO duality; size, independence and number of meetings of the board; size, independence and the number of meetings of the audit committee; type as well as the concentration of ownership. Data for all governance variables of a sample company was collected for the year in which the company was granted the credit rating. An elaborate description of these has been provided in Table 3.

Control Variables: Firm-Specific Risk Characteristics

Based on a review of the literature on credit ratings, several firm-specific risk factors were identified that have an impact on the credit ratings (Alali et al., 2012; Aman & Nguyen, 2013; Ashbaugh-Skaife et al., 2006; Bhojraj & Sengupta, 2003; Boardman & McEnally, 1981; Kaplan & Urwitz, 1979; Lamy & Thompson, 1988; Horrigan, 1966). These include leverage (LEV), return on assets (ROA), interest coverage ratio (INT_COV), firm size (FSIZE) as well as size of the debt issued (LSIZE).

LEV, ROA and INT_COV are the proxies of a firm’s default risk, whereby higher LEV and lower ROA and INT_COV indicate greater default risk (Aman & Nguyen, 2013; Ashbaugh-Skaife et al., 2006). The log of size of the debt issued has also been included since the scale economies in case of underwriting indicate that the size of debt issued will possess an inverse relation with bond yields and a positive one with credit ratings (Bhojraj & Sengupta, 2003). Finally, log of the size of the firm was also included since firms that are large sized experience lower risk and generally tend to have higher ratings as compared to firms that are smaller in size (Aman & Nguyen, 2013; Ashbaugh-Skaife et al., 2006). Data for all control variables of a sample company was collected for the year in which the company was granted the credit rating. A detailed description of these variables has been provided in Table 3.

Table 3.

Variable Specification

S. No.	Variable	Symbol Used	Definition	Source
Panel A: Corporate Governance Variables
I. Board Characteristics
1.	CEO Duality	CEODUAL	Dummy variable, ‘1’ if the CEO is also the Chairman of the board, ‘0’ otherwise	Annual Reports
2.	Board Size	BSIZE	Total number of directors on the board	Annual Reports
3.	Board Independence	BIND	Percentage of independent directors on the board	Annual Reports
4.	Board Meetings	BMEET	Number of board meetings during the year	Annual Reports
II. Audit Committee Characteristics
5.	Audit Committee Size	ASIZE	Number of directors on the audit committee	Annual Reports
6.	Audit Committee Independence	AIND	Percentage of independent directors in the audit committee	Annual Reports
7.	Audit Committee Meetings	AMEET	Number of audit committee meetings during the year	Annual Reports
III. Ownership Pattern
8.	Ownership Type	OT	Dummy variable: ‘1’ if Indian Promoters are the largest shareholders and ‘0’ in case of Non-Institutional Public Shareholders	Annual Reports
9.	Ownership Concentration	OCON	Herfindahl Index: Sum of squared percentage of shareholding of the top three promoters	Annual Reports
Panel B: Firm Characteristics (Controls)
1.	Leverage	LEV	Ratio of Total Debt to Total Assets	Prowess
2.	Return onAssets	ROA	Ratio of Net Income to Total Assets	Prowess
3.	Interest Coverage	INT_COV	Ratio of Earnings Before Interest and Taxes (EBIT) to the Interest expense	Prowess
4.	Firm Size	FSIZE	Natural logarithm of Total Assets	Prowess
5.	Issue Size	LSIZE	Natural logarithm of the size of the debt issued	Prowess

Source: Drawn from literature.

Data Analyses

In order to analyse the relationship between corporate governance and credit ratings, ordinal logistic regression was used. This was because the categorisation of credit ratings, the dependent variable, into seven distinct classes as shown in Table 2 conveys risk assessments that are ordinal in nature. This implies that although a firm’s preference across these categories can be ranked but uniform differences between any two categories cannot be assumed.

The assumption of proportional odds is the key assumption in case of ordinal logistic regression. To check for this assumption, the test of parallel lines was invoked. The p-value of chi-square (p = .302) was found to be greater than 0.05 implying that the assumption holds true and ordinal logistic regression is applicable to the dataset.

Also, to carry out ordinal regression, there should be no multicollinearity in the dataset. To check for multicollinearity, Variance Inflation Factor (VIF) and Tolerance were used. The coefficients of VIF less than 10 and Tolerance values more than 0.1 for all the variables indicated that multicollinearity was not a problem in the dataset. This is also evident from the low magnitude of correlation amongst the variables as presented in Table 5.

Further, binary logistic regression analysis was used to calculate the marginal or incremental effect of the changes in corporate governance variables on the credit ratings by employing a separate classification scheme under which the ratings were divided into two categories—speculative grade and investment grade. This was required since it is difficult to calculate such effects of any independent variable on the dependent variable having multiple categories.

Following regression model was used to assess the relation between corporate governance and credit ratings:

\begin{matrix} CR / {INV}_{-} GRADE = β_{0} + β_{1} (CEODUAL) + β_{2} (BSIZE) + β_{0} (BIND) + β_{4} \\ (BMEET) + β_{5} (ASIZE) + β_{6} (AIND) + β_{7} (AMEET) + β_{8} (OT) + β_{9} (OCON) + \\ β_{10} (LEV) + β_{11} (ROA) + β_{12} ({INT}_{-} COV) + β_{13} (FSIZE) + β_{14} (LSIZE) \end{matrix}

(1)

CR and INV_GRADE are the dependent variables representing credit rating for the ordinal and binary logistic regression analysis respectively. CEODUAL, BSIZE, BIND, BMEET, ASIZE, AIND, AMEET, OT and OCON are the independent variables representing corporate governance. LEV, ROA, INT_COV, FSIZE and LSIZE are the control variables representing firm-specific risk characteristics.

The software packages ‘SPSS’ (Version 23) and ‘STATA’ (Version 13) were used to carry out the data analysis in the present study.

Descriptive Statistics

Table 4 provides the descriptive data for the sample companies for all the variables under study. Panel A of Table 4 reveals the descriptive data for the dependent variables under study. CR has a mean value of 3.84 meaning that an average firm in the sample received a rating from BBB+ to BB−. Also, the median value is 4 indicating that 50 per cent of the sample received a credit rating above BBB−. INV_GRADE has a mean value of 0.62 implying that 62 per cent of the firms received an investment grade credit rating.

Panel B shows the descriptives for the independent, that is, corporate governance variables under study. CEODUAL has an arithmetic mean of 0.5 meaning that 50 per cent of the firms in the sample have the same person as the CEO as well as the chairman of the board. The board size ranged from a lower quartile (Q1) of six to an upper quartile (Q3) of nine members, averaging out at about seven members for the sample. The mean of BIND is 52 per cent which implies that on an average, 52 per cent of the board members were independent directors. BMEET ranged from Q1 of five to Q3 of seven, having a mean value of around six meetings and a standard deviation of around two. ASIZE had an interquartile range of one with a mean of around three, implying that the audit committee for the sample had three members on an average. AIND ranged from 66.67 per cent to as high as 100 per cent in the quartiles resulting in a mean value of 79 per cent implying that on an average 79 per cent of the audit committee members were independent directors. Also, AMEET ranged from four to five in the quartiles with a mean of around four. OT had a mean of 0.76 meaning that 76 per cent of the companies had a majority of Indian promoters as shareholders and only 24 per cent had a majority of non-institutional public shareholders. The mean OCON value of 0.11 indicates moderately concentrated shareholding among the promoters.

Table 4.

Descriptive Statistics

Panel A: Credit Ratings
	Mean	Median	Std. Deviation	Q1	Q3
CR	3.84	4.00	1.33	3	5
INV_GRADE	0.62	1.00	0.49	0	1
Panel B: Corporate Governance Variables
	Mean	Median	Std. Deviation	Q1	Q3
CEODUAL	0.50	0.00	0.50	0	1
BSIZE (No.)	7.52	7.00	2.39	6	9
BIND (%)	52.00	50.00	8.62	50	57.14
BMEET (No.)	6.43	6.00	2.61	5	7
ASIZE (No.)	3.59	3.00	0.80	3	4
AIND (%)	79.08	75.00	14.26	66.67	100
AMEET (No.)	4.66	4.00	1.35	4	5
OT	0.76	1.00	0.43	1	1
OCON	0.11	0.08	0.11	0.03	0.16
Panel C: Firm Characteristics
	Mean	Median	Std. Deviation	Q1	Q3
LEV (%)	15.84	12.97	15.65	2.51	22.03
ROA (%)	4.55	3.58	4.96	1.51	6.75
INT_COV (Times)	34.90	2.32	365.49	1.44	4.90
FSIZE(₹ millions)	51857.56	2567.80	497192.77	961.90	9074.60
LSIZE(₹ millions)	2135.69	150	12707.71	32.80	540.70

Source: Author’s own calculations.

Panel C shows the descriptives for the control variables under study, that is, firm-specific risk characteristics. The lower and upper quartile values of LEV depict that the sample had companies where the total debt as a percentage of total assets ranged from 2.51 to 22.03 per cent in the quartiles. Similarly, the first and third quartile values of ROA depict that the sample had companies with ROA ranging from 1.51 to 6.75 per cent in the quartiles. INT_COV had an interquartile range of 3.46 with an average of 34.90 times. The average firm had total assets (FSIZE) of around ₹51,857 million. Also, the size of the debt issued (LSIZE) varied from ₹32.80 million to ₹540.70 million in the quartiles.

Correlation Analysis

Table 5 reports Spearman correlations among the variables under study. Amongst the variables representing board characteristics, CEODUAL has a significant negative association with CR implying a low rating in case of firms with CEO Duality. BSIZE on the other hand has a significant positive association with CR meaning that firms with a larger board size as compared to others tend to receive higher ratings. BIND and BMEET, however, do not seem to have a significant relation with CR.

Next, in case of the variables representing audit committee characteristics, ASIZE and AIND have a significant positive relationship with CR implying a higher rating in case of firms where audit committee is large in size and has a greater representation of independent directors. AMEET, however, does not seem to have a significant relation with CR.

Amongst the variables representing ownership structure, OT has a significant positive association with CR meaning that firms with a majority of Indian promoters, in the ownership structure, tend to get better ratings as compared to the firms with a majority of non-institutional public shareholders. However, OCON does not seem to have a significant relation with CR. Finally, in case of the control variables, ROA, INT_COV, LSIZE as well as FSIZE have a significant positive association with CR. However, LEV does not seem to have a significant relation with CR.

Empirical Results and Discussion

Results of the Ordinal Logistic Regression Analysis

The results of the ordinal logistic regression, used to test the association between corporate governance variables and credit ratings, while controlling for a set of firm-specific risk characteristics, have been summarised in Table 6. Following Asbaugh-Skaife et al. (2006), three regression models were constructed. The first model was framed to jointly test the association of governance and firm-specific risk characteristics with credit ratings. The second model, where firm-specific risk characteristics were taken in isolation, was constructed to benchmark results against prior studies on credit ratings as well as to assess the impact of omission of governance variables on the explanatory power of the first model. Finally, the third model, where just the governance variables were taken, was constructed in order to compare the explanatory power of those variables with that of the firm-specific risk characteristics taken in the second model. Also, this was done in order to assess if the governance variables explain a significant amount of the cross-sectional variation in credit ratings.

Table 5.

Correlation Analysis

Panel A: Firm Characteristics

LEV

ROA

INT_COV

FSIZE

LSIZE

1.000

LEV

−0.121

1.000

ROA

0.414^**

−0.263^**

1.000

INT_COV

0.425^**

−0.388^**

0.881^**

1.000

FSIZE

0.496^**

0.138

0.193^*

0.182^*

1.000

LSIZE

0.424^**

0.106

0.107

0.099

0.532^**

1.000

Panel B: Corporate Governance Variables

CEODUAL

BSIZE

BIND

BMEET

ASIZE

AIND

AMEET

OCON

1.000

CEODUAL

−0.208^*

1.000

BSIZE

0.436^**

−0.277^*

1.000

BIND

0.085

0.099

−0.178^**

1.000

BMEET

−0.157

0.005

−0.140

−0.128

1.000

ASIZE

0.349^**

−0.297^**

0.418^**

0.100

−0.245^**

1.000

AIND

0.276^**

−0.039

0.274^**

0.213^**

−0.153

0.244^**

1.000

AMEET

0.072

−0.136

−0.041

−0.077

0.342^**

−0.024

−0.109

1.000

0.283^**

−0.170^*

0.054

0.079

−0.095

0.128

0.173^*

0.055

1.000

OCON

0.130

0.071

−0.210^*

0.118

−0.017

−0.005

0.166^*

0.039

0.342^**

1.000

Source: Author's analysis.

Notes:

1. *, ** indicates that the correlation is significant at 5 and 1 per cent level, respectively.

2. CR = CRISIL Long Term Debt Credit Rating. See Table 2 for numeric coding.

3. See Table 3 for other variable definitions.

The ‘Model 1’ column of Table 6 reports the results for all the variables under study whereby both the corporate governance variables as well as the firm-specific risk characteristics were jointly tested to explore their association with credit ratings. The model was found to be highly significant with a pseudo R-square of 59.1 per cent. Also, it was found that BSIZE has a positive impact on CR which is significant at the 1 per cent level. This implies that firms with larger board sizes tend to receive higher credit ratings as compared to their counterparts. AIND has a weak positive effect on CR that is significant at the 10 per cent level thus implying that firms where there are more independent directors in the audit committee, as compared to other companies, tend to receive higher credit ratings.

Table 6.

Ordinal Logistic Regression Results: Effect of Corporate Governance on Credit Ratings (CR)

Variables	Estimated Coefficient
Variables	Model 1	Model 2	Model 3
Corporate Governance Variables
CEODUAL	−0.058(0.027)		−0.262(0.676)
BSIZE	0.255***(7.694)		0.378***(19.294)
BIND	−0.003(0.016)		0.033*(3.156)
BMEET	−0.040(0.416)		−0.050(0.690)
ASIZE	0.165(0.497)		0.110(0.248)
AIND	0.022*(3.332)		0.014(1.476)
AMEET	0.227(2.455)		0.265**(4.021)
OT	0.827**(4.289)		0.709*(3.445)
OCON	0.142(0.881)		0.272*(3.753)
Firm Characteristics
LEV	−0.034***(8.872)	−0.033***(9.042)
ROA	0.148***(16.559)	0.138***(16.107)
INT_COV	0.001(0.749)	0.000(.749)
FSIZE	0.574***(20.962)	0.648***(30.476)
LSIZE	0.149*(2.733)	0.241***(9.070)
Pseudo R-Square	0.591	0.483	0.346
Likelihood Ratio Chi-Square	130.578***	97.117***	62.832***

Notes:

1. The values in the table above have been derived from the following ordinal logistic regression models:

a. Model 1: CR = f (corporate governance variables, firm characteristics)

b. Model 2: CR = f (firm characteristics)

c. Model 3: CR = f (corporate governance variables)

2. *, ** and *** indicate significance at 10, 5 and 1 per cent level, respectively.

3. Figures in parentheses are Wald-statistics of the explanatory variables.

4. The variables above have been explained in Table 3.

Source: Author's analysis.

Also, OT was found to have a strong positive effect on CR that was significant at the 5 per cent level implying that firms with a majority shareholding of Indian promoters get better ratings as compared to the firms where majority shareholding resides with the non-institutional public. Amongst the control variables, LEV was reported to have a significant negative impact on credit ratings whereas other variables, that is, ROA, FSIZE and LSIZE were found to have a significant positive impact on credit ratings.

The ‘Model 2’ column of Table 6 indicates the regression results obtained for just the control variables under study. This model was found to be significant at the 1 per cent level. Further, it was found that when excluding the corporate governance variables from the regression model, the pseudo R-square declines from 59.1 to 48.3 per cent.

The ‘Model 3’ column of Table 6 shows the results of running the regression model using just the governance variables. This model too was found to be significant at the 1 per cent level. Further, another interesting observation was that the variation in credit ratings that is explicated by corporate governance characteristics is nearly as much as the variation explained by the firm-specific risk characteristics (pseudo R-square of 34.6 per cent versus 48.3 per cent). Hence, a major chunk of the cross-sectional variation in the credit ratings is explained by the corporate governance variables.

Results of the Binary Logistic Regression Analysis

Table 7 shows the results of the binary logistic regression analysis that was conducted using ‘INV_GRADE’ as the dependent variable in Equation (1). The new variable used to capture credit ratings, that is, ‘INV_GRADE’ was coded as ‘1’ if the credit rating received by the firm was BBB or higher, and ‘0’ otherwise. The model was found to be significant at the 1 per cent level. Also, the p-value in the ‘Hosmer and Lemeshow Goodness of Fit Test’ was found to be greater than 0.05, suggesting that the model was a good fit. The results found in this case were similar to that of the ordinal logistic regression analysis, reported in Table 6, with respect to BSIZE, OT, LEV, ROA and FSIZE. Also, the coefficient on ASIZE was significant at the 5 per cent level whereas it was found to be insignificant in the analysis done in the previous section. Further, the coefficients with respect to AIND and LSIZE were no longer found to be significant when INV_GRADE was used as the proxy for credit ratings.

In order to get additional insights into the economic significance of the regression results, the variation in the chance of getting a rating in the investment grade by way of modifying the governance variables under study, was calculated from the logistic regression model. These marginal effects have been shown in Table 8. They depict the increase or decrease in the chasnce of getting a rating within the investment grade, that is, BBB or higher, for a unit change in the governance variable under study, while fixing the other variables at their respective means. The marginal effect of the significant corporate governance variables, that is, BSIZE, ASIZE and OT, was found to be 6 per cent, 14 per cent and 25 per cent, respectively.

Table 7.

Results of Binary Logistic Regression Analysis: Effect of Corporate Governance on Credit Ratings (INV_GRADE)

Variables	Estimated Coefficient	Wald Statistic
Intercept	−7.115**	6.260
CEODUAL	0.326	0.468
BSIZE	0.290**	4.237
BIND	−0.005	0.040
BMEET	−0.016	0.039
ASIZE	0.691**	3.868
AIND	−0.006	0.160
AMEET	−0.022	0.010
OT	1.117**	4.275
OCON	0.054	0.074
LEV	−0.030*	3.660
ROA	0.098**	4.064
INT_COV	0.000	0.027
FSIZE	0.516***	9.081
LSIZE	0.052	0.186
Pseudo R-Square	0.453
Chi-Square	62.772***

Notes:

1. *, ** and *** indicate significance at 10, 5 and 1 per cent level, respectively.

2. ‘INV_GRADE’ was coded as ‘1’ if the credit rating received by the firm was BBB or higher, and ‘0’ otherwise, as shown in Table 2.

3. The variables above have been explained in Table 3.

Source: Author's analysis.

Another way of quantifying the impact of the corporate governance variables on the probability of getting an INV_GRADE rating is by way of computing the difference in the chance of getting such rating when the corporate governance variable of interest takes up the lower quartile value (Q1) from the chance of getting the rating when the same corporate governance variable takes up the upper quartile value (Q3), while at the same time holding all other variables (corporate governance variables excluding the variable under study and the controls) fixed at their respective means. These differences have been reported in Table 8.

Moving from the values in the first quartile towards the values in the third quartile of the significant corporate governance variable ‘BSIZE’, it was found that the probability of falling in the INV_GRADE increases by 18 per cent. In case of the other significant variables, that is, ASIZE and OT, the same was found to be 15 and 25 per cent, respectively. Although the fluctuations in the probability of getting an INV_GRADE rating, occurring because of a standalone corporate governance variable, might not appear to be significant, the aggregate change in all the corporate governance variables may turn out to be substantial. It was found that this probability almost doubled, increasing by 42 per cent from 48 to 90 per cent, when moving from the first (third) quartile values of the variables of corporate governance that had a positive (negative) relation with credit ratings to their third (first) quartile values.

Table 8.

Calculation of the Changes in Probabilities of Receiving an Investment Grade Rating

Variable	Marginal Effect	Change in Probability(Q1 vs. Q3 Values)
CEODUAL	0.07	0.07
BSIZE	0.06	0.18
BIND	0.00	−0.01
BMEET	0.00	−0.01
ASIZE	0.14	0.15
AIND	0.00	−0.04
AMEET	0.00	0.00
OT	0.25	0.25
OCON	0.01	0.02

Notes:

1. The marginal effect depicts the increase or decrease in the probability of getting a rating within the investment grade, that is, BBB or higher, for a unit change in the variable of interest, while keeping the other variables constant at their respective mean values.

2. Values in the Change in probability column show the change in the probability of receiving an investment-grade credit rating as a result of moving from the first to the third quartile value of the variable of interest, holding all other variables constant at their mean values.

3. The variables above have been explained in Table 3.

Source: Author's analysis.

Discussion

The results of the analyses show that some of the governance characteristics do significantly affect the credit ratings. In case of the results of the ordinal logistic regression analysis, BSIZE impacted CR positively implying that firms with larger board sizes tend to receive better ratings and thus upholding H_1a. A similar result was reported by Aman and Nguyen (2013) who found that credit ratings tend to increase with board size because larger boards generally take up less risky investments owing to a greater discussion and diversity of views in such boards. Similarly, AIND impacted the CR in a positive manner, implying that companies that had a major percentage of independent directors in the audit committee, as compared to other companies, tend to receive higher ratings, and therefore, lending support to H_1f. This is consistent with the findings of Anderson et al. (2004) who reported that audit committees that are fully independent are viewed favourably by the debtholders. Also, OT impacted CR positively implying that firms with majority shareholding of Indian promoters tend to receive better credit ratings in comparison to their peers where majority shareholding is with the non-institutional public, thus upholding H_1i. This is consistent with the fact that concentrated shareholding has crucial importance in the proper working of the system of governance since such shareholders, that is, Indian promoters in this case, have a financial interest as well as the necessary voting power to put pressure on the management for a corrective action whenever required as compared to a widely held shareholding by the public (Gordon & Pound, 1993; Jensen, 1993; Shleifer & Vishny, 1997). Several other studies have also found a significant positive impact of the concentration of ownership on the credit ratings of a firm (Aman & Nguyen, 2013; Ashbaugh-Skaife et al., 2006; Bhojraj & Sengupta, 2003). The calculation of change in the chance of falling in the INV_GRADE, by employing binary logistic regression analysis, revealed that this probability almost doubled, increasing by 42 per cent from 48 to 90 per cent, when moving from the first (third) quartile values of the corporate governance variables that had a positive (negative) relation with credit ratings to their third (first) quartile values. Ashbaugh-Skaife et al. (2006) in their study of US firms reported a similar result.

Issue of Endogeneity

Endogeneity is a basic problem that plagues the papers on corporate governance (Alali et al., 2012). Two-stage least squares regression offers a solution to the problem of endogeneity whereby instrumental variables are identified and are then used to predict values of the endogenous variables in the first stage, which are then regressed on the dependent variable in the second stage (Wooldridge, 2016). The instrumental variables approach helps address any omitted variable bias as well as simultaneous causality bias that might be leading to the endogeneity problem (Bascle, 2008). Following the methodology of Ashbaugh-Skaife et al. (2006), the past performance was taken as the instrumental variable. It was measured as the stock returns (PP_RET) over the previous 1-, 3- and 5-year terms in Models 4, 5 and 6 respectively. Table 9 presents the results after including the past performance variable in the base model (Model 1 of Table 6).

Table 9.

Sensitivity Tests using Prior Period Performance

Variables	Estimated Coefficient
Variables	(Model 4)Prior 1-YearPerformance	(Model 5)Prior 3-YearPerformance	(Model 6)Prior 5-YearPerformance
Corporate Governance Variables
CEODUAL	−0.028(.006)	−0.019(3.236)	−0.025(0.005)
BSIZE	0.223**(5.483)	0.230**(5.853)	0.229**(5.911)
BIND	−0.004(.045)	−0.004(0.029)	−0.004(0.031)
BMEET	−0.063(.926)	−0.062(0.905)	−0.065(0.965)
ASIZE	0.166(0.464)	0.153(0.404)	0.158(0.428)
AIND	0.023*(3.582)	0.023*(3.554)	0.023*(3.553)
AMEET	0.314**(3.849)	0.321**(4.004)	0.321**(4.023)
OT	0.741*(3.289)	0.735*(3.236)	0.724*(3.145)
OCON	0.062(0.153)	0.069(0.194)	0.069(0.195)
Firm Characteristics
LEV	−0.029**(5.956)	−0.029**(5.840)	−0.029**(5.855)
ROA	0.165***(18.017)	0.165***(17.843)	0.165***(17.952)
INT_COV	0.001(0.852)	0.001(.824)	0.001(0.832)
FSIZE	0.524***(17.197)	0.520***(16.972)	0.517***(16.786)
LSIZE	0.152*(2.816)	0.150*(2.719)	0.151*(2.763)
PP_RET	0.017(0.186)	0.009(0.022)	0.020(0.115)
Pseudo R-Square	0.578	0.578	0.578
Likelihood Ratio Chi-Square	117.443***	117.293***	117.395***

Notes:

1. The values in the table above have been derived from the following ordinal logistic regression models:

a. Model 4: CR = f (corporate governance variables, firm characteristics, prior 1-year performance)

b. Model 5: CR = f (corporate governance variables, firm characteristics, prior 3-year performance)

c. Model 6: CR = f (corporate governance variables, firm characteristics, prior 5-year performance)

2. *, ** and *** indicate significance at 10, 5 and 1 per cent level, respectively.

3. Figures in parentheses are Wald-statistics of the explanatory variables.

4. The variables above have been explained in Table 3. PP_RET is the average stock return over the past 1 year, 3 years and 5 years in the Models 4, 5 and 6, respectively.

Source: Author's analysis.

Including the measures of past performance in the base model along with the corporate governance variables is equivalent to the two-stage procedure and is simpler to use given the large number of governance variables (Ashbaugh-Skaife et al., 2006). The results were very similar to the results originally found. All the variables that were significant in the base model retained their significance. Hence, the inclusion of ‘past performance’ did not significantly affect the results relating to the impact of corporate governance on credit ratings.

Conclusion and Implications

Effective governance mechanisms can affect the credit ratings of a firm by way of their influence on a firm’s default risk. Corporate governance mechanisms, by way of effective supervision of the decisions as well as actions of the management, tend to reduce the interest conflict that arises between the providers of capital, including the shareholders as well as the debtholders of a firm, and the management. Hence, these mechanisms are viewed positively by the debtholders and result in a lowering the of firm’s default risk.

The present article aimed to discern the association between corporate governance and credit ratings by studying the firms that received a rating from CRISIL for their long-term debt during any of the 5 years from 2013–2014 to 2017–2018. ordinal logistic regression was used to carry out the analysis by classifying the credit ratings into seven categories. Also, binary logistic regression analysis was used in order to calculate the marginal impact of the modifications in the governance variables on the credit ratings by employing a separate classification scheme under which the ratings were divided into two categories—speculative grade and investment grade.

The results showed that some corporate governance characteristics, namely size of the board, independence of the audit committee and the type of ownership do have a significant positive impact on the ratings in case of Indian firms. Also, as the corporate governance variables having a positive (negative) association with the ratings switched values from their first (third) quartile to their third (first) quartile, the chance of a firm getting a rating within the investment grade increased by 42 per cent, from 48 to 90 per cent.

The results, however, ought to be read with caution owing to the presence of some inherent limitations. First of all, the study has a limited scope since it takes into account the long-term debt issues of just 5 years period. The findings would have been more rigorous if more time periods were taken into account. Also, the study takes into account just the long-term debt instruments. The effect of corporate governance on other debt instruments has not been explored in the present study. Also, the ratings awarded by CRISIL were used for the present study. However, since the ratings of different agencies do not tend to be highly correlated, studying the effect of corporate governance on the credit ratings awarded by a different agency may yield different results. Finally, the study took into account the initial credit ratings granted for the debt issued by companies in the sample period, hence the lag effects of past credit ratings on the corporate governance and firm-specific risk characteristics variables could not be captured.

Nevertheless, the findings do have implications for both the corporations as well as the investors particularly debtholders since they highlight a significant association between governance and credit ratings. Good corporate governance can benefit companies by allowing them to have access to funds in large amounts but at a lower cost as compared to their peers. Also, good governance practices can help mitigate the agency problem and hence nullify the risks that are faced by the debtholders of a firm.

Future research efforts can focus on finding the effect of governance on the ratings issued by a different agency. Also, studies on finding the impact of governance on the ratings of other debt instruments can be taken up. Future research can also focus on using the composite corporate governance scores that are being compiled by several agencies nowadays and testing the association between such scores and the credit ratings.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

ORCID iD

Taruntej Singh Arora

References

Adams

Ferreira

(2010). Moderation in groups: Evidence from betting on ice break-ups in Alaska. The Review of Economic Studies , 77(3), 882–913.

Alali

Anandarajan

Jiang

(2012). The effect of corporate governance on firm’s credit ratings: Further evidence using governance score in the United States. Accounting & Finance , 52(2), 291–312.

Aman

Nguyen

(2013). Does good governance matter to debtholders? Evidence from the credit ratings of Japanese firms. Research in International Business and Finance , 29, 14–34.

Anderson

R. C.

Mansi

S. A.

Reeb

D. M.

(2004). Board characteristics, accounting report integrity, and the cost of debt. Journal of Accounting and Economics , 37(3), 315–342.

Arora

Sharma

(2016). Corporate governance and firm performance in developing countries: Evidence from India. Corporate Governance: The International Journal of Business in Society , 16(2), 420–436.

Ashbaugh-Skaife

Collins

D. W.

LaFond

(2006). The effects of corporate governance on firms’ credit ratings. Journal of Accounting and Economics , 42(1–2), 203–243.

Bansal

Sharma

A. K.

(2019). Corporate governance and firm performance in an emerging economy context: New evidence from India. International Journal of Comparative Management , 2(2), 123–147.

Bascle

(2008). Controlling for endogeneity with instrumental variables in strategic management research. Strategic Organization , 6(3), 285–327.

Beasley

M. S.

(1996). An empirical analysis of the relation between the board of director composition and financial statement fraud. Accounting Review , 71(4), 443–465.

10.

Bhojraj

Sengupta

(2003). Effect of corporate governance on bond ratings and yields: The role of institutional investors and outside directors. The Journal of Business , 76(3), 455–475.

11.

Boardman

C. M.

McEnally

R. W.

(1981). Factors affecting seasoned corporate bond prices. Journal of Financial and Quantitative Analysis , 16(2), 207–226.

12.

Chakrabarti

Subramanian

Tung

(2010). Independent directors and firm value: Evidence from an emerging market. https://ssrn.com/abstract=1631710

13.

Cheng

(2008). Board size and the variability of corporate performance. Journal of Financial Economics , 87(1), 157–176.

14.

Coffee

J. C.

(2006). Gatekeepers: The professions and corporate governance . Oxford University Press.

15.

Dwivedi

Jain

A. K.

(2005). Corporate governance and performance of Indian firms: The effect of board size and ownership. Employee Responsibilities and Rights Journal , 17(3), 161–172.

16.

Elbannan

M. A.

(2009). Quality of internal control over financial reporting, corporate governance and credit ratings. International Journal of Disclosure and Governance , 6(2), 127–149.

17.

Fama

E. F.

Jensen

M. C.

(1983). Separation of ownership and control. The Journal of Law and Economics , 26(2), 301–325.

18.

Gordon

L. A.

Pound

(1993). Information, ownership structure, and shareholder voting: Evidence from shareholder sponsored corporate governance proposals. The Journal of Finance , 48(2), 697–718.

19.

Goyal

V. K.

Park

C. W.

(2002). Board leadership structure and CEO turnover. Journal of Corporate Finance , 8(1), 49–66.

20.

Guo

Masulis

R. W.

(2015). Board structure and monitoring: New evidence from CEO turnovers. The Review of Financial Studies , 28(10), 2770–2811.

21.

Hermalin

B. E.

Weisbach

M. S.

(1998). Endogenously chosen boards of directors and their monitoring of the CEO. American Economic Review , 88(1), 96–118.

22.

Horrigan

J. O.

(1966). The determination of long-term credit standing with financial ratios. Journal of Accounting Research , 4, 44–62.

23.

Jackling

Johl

(2009). Board structure and firm performance: Evidence from India’s top companies. Corporate Governance: An International Review , 17(4), 492–509.

24.

Jaiswall

S. S. K.

Bhattacharyya

A. K.

(2016). Corporate governance and CEO compensation in Indian firms. Journal of Contemporary Accounting & Economics , 12(2), 159–175.

25.

Jensen

M. C.

(1993). The modern industrial revolution, exit, and the failure of internal control systems. Journal of Finance , 48(3), 831–880.

26.

Kaplan

R. S.

Urwitz

(1979). Statistical models of bond ratings: A methodological inquiry. Journal of Business , 52(2), 231–261.

27.

Klein

(2002). Audit committee, board of director characteristics, and earnings management. Journal of Accounting and Economics , 33(3), 375–400.

28.

Kumar

Singh

J. P.

(2013). Effect of board size and promoter ownership on firm value: Some empirical findings from India. Corporate Governance: The International Journal of Business in Society , 13(1), 88–98.

29.

Lamy

R. E.

Thompson

G. R.

(1988). Risk premia and the pricing of primary issue bonds. Journal of Banking & Finance , 12(4), 585–601.

30.

Lehn

K. M.

Patro

Zhao

(2009). Determinants of the size and composition of US corporate boards: 1935-2000. Financial Management , 38(4), 747–780.

31.

Lipton

Lorsch

J. W.

(1992). A modest proposal for improved corporate governance. The Business Lawyer , 48, 59–77.

32.

Lorca

Sánchez-Ballesta

J. P.

García-Meca

(2011). Board effectiveness and cost of debt. Journal of Business Ethics , 100(4), 613–631.

33.

Menon

Williams

J. D.

(1994). The use of audit committees for monitoring. Journal of Accounting and Public Policy , 13(2), 121–139.

34.

Mishra

Malhotra

A. K.

(2016). Audit committee characteristics and earnings management: Evidence from India. International Journal of Accounting and Financial Reporting , 6(2), 247–273.

35.

Mishra

Kapil

(2017). Effect of ownership structure and board structure on firm value: Evidence from India. Corporate Governance: The International Journal of Business in Society , 17(4), 700–726.

36.

Nashier

Gupta

(2020). Ownership concentration and firm performance in India. Global Business Review . https://doi.org/10.1177/0972150919894395

37.

Partnoy

(2006). How and why credit rating agencies are not like other gatekeepers. In Fuchita

Litan

R. E.

(Eds.), Financial gatekeepers: Can they protect investors? (pp. 59-99). Brookings Institution Press. https://ssrn.com/abstract=900257

38.

Pincus

Rusbarsky

Wong

(1989). Voluntary formation of corporate audit committees among NASDAQ firms. Journal of Accounting and Public Policy , 8(4), 239–265.

39.

Roychowdhury

Srinivasan

(2019). The role of gatekeepers in capital markets. Journal of Accounting Research , 57(2), 295–322.

40.

Sengupta

(1998). Corporate disclosure quality and the cost of debt. Accounting Review , 73(4), 459–474.

41.

Shleifer

Vishny

R. W.

(1997). A survey of corporate governance. The Journal of Finance , 52(2), 737–783.

42.

Shrivastav

S. M.

Kalsie

(2016). The relationship between CEO duality and firm performance: An analysis using panel data approach. IUP Journal of Corporate Governance , 15(2). 37–58.

43.

Vafeas

(1999). Board meeting frequency and firm performance. Journal of Financial Economics , 53(1), 113–142.

44.

Weinstein

(1981). The systematic risk of corporate bonds. Journal of Financial and Quantitative Analysis , 16(3), 257–278.

45.

West

R. R.

(1970). An alternative approach to predicting corporate bond ratings. Journal of Accounting Research , 8(1), 118–125.

46.

Wooldridge

J. M.

(2016). Introductory econometrics: A modern approach . Nelson Education.

47.

Xie

Davidson

W. N.

DaDalt

P. J.

(2003). Earnings management and corporate governance: The role of the board and the audit committee. Journal of Corporate Finance , 9(3), 295–316.

48.

Yermack

(1996). Higher market valuation of companies with a small board of directors. Journal of Financial Economics , 40(2), 185–211.