Corporate Governance and Cash Holdings in Indian Firms

Governance and Regulations’ Contemporary Issues

ISBN: 978-1-78743-816-3, eISBN: 978-1-78743-815-6

ISSN: 1569-3759

Publication date: 9 July 2018

Abstract

A persistent and increasing pattern in cash holdings was notable in the aggregate behaviour of Indian corporations around the period from 2007–2008 to 2012–2013. Extant literature suggests that agency conflicts and financing frictions are important determinants of cash holdings. In this chapter the author aims to shed light on the role of corporate governance (CG) in the determination of cash holdings and examined how ownership structure, board and audit-related attributes (used as proxies for the nature of CG) impact cash holdings in the context of an emerging economy, like India. The author employed four different measures of cash and liquidity and 24 structural indicators of CG. Using principal component analysis, the author offers an exploratory inquiry into the dimensions of CG. Thereafter, multiple regression was used to delve into the association between cash holdings (the dependent variable) and CG. Using a sample of 58 top-listed companies the results revealed that the quality of firm-level CG is important in deciding corporate cash holdings. The author reported that firms with stronger CG tend to reduce cash balances and have higher capital expenditures, while in firms with entrenched managers having high cash reserves invest more in current assets. Firms also hold cash for financial flexibility and to take advantage of strategic opportunities as they present themselves. Parallel to this point is the fact that larger balances help firms to avoid uncertainty and hedge themselves against the difficulty of accessing external funds.

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Citation

Roy, A. (2018), "Corporate Governance and Cash Holdings in Indian Firms", Grima, S. and Marano, P. (Ed.) Governance and Regulations’ Contemporary Issues (Contemporary Studies in Economic and Financial Analysis, Vol. 99), Emerald Publishing Limited, pp. 93-119. https://doi.org/10.1108/S1569-375920180000099005

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Emerald Publishing Limited

Copyright © 2018 Emerald Publishing Limited


1. Introduction

Blue chip Indian companies had large cash balances around the period from 2007–2008 to 2012–2013. The figures shown in financial statements were mind boggling. The Economic Times reported (on August 20, 2012) that top 500 listed companies owned enough cash to double the power generation capacity of 2,00,000 mw or build over 40,000 km of six-lane highways every year (compared with the then standard of 800 km). The private sector behemoth, Reliance Industries, topped the list with C37,984 crores. Within the Tata group, Tata Motors had the largest cash reserves of C29,712 crores. In the information technology sector, Infosys reported that its cash balance had more than doubled from C10,556 crores in 2010 to C25,950 crores in 2012–2013. TCS, Wipro, HCL Technologies also had cash holding between C10,000 crores and C20,000 crores. Among public sector enterprises (PSE) Coal India reported a balance amounting to more than C52,389 crores. Further, if we consider listed companies over the period of 10 years, from 2004 to 2014, cash reserves had increased by ninefold.

This phenomenon is common the world over. Bates, Kahle and Stulz (2009) found that there was an increase in cash holdings of US firms from 1980 to 2006. Continuing with US firms, in 2011 cash amounted to $5 trillion, and this was more than for any other year since 1980 (Sánchez & Yurdagül, 2013). Gao, Harford, and Li (2013) noted that as of 2011, public firms in the USA held on average 20.45% of their assets in cash or cash equivalents (hereafter, CCE). Similar increasing trends were reported by UK firms (Ozkan & Ozkan, 2004 ). Country-specific studies related to firms in Germany, Japan and Southeast Asia have also pointed out that firms in these countries have also stuck to this trend of accumulating cash (Opler, Pinkowitz, Stulz, & Williamson, 1999). Ammann, Oeschb and Schmid (2011) offered compelling evidence on increased cash holdings by firms around the world from 46 emerging and developed countries.

Why do firms hold copious amounts of cash? The focus on corporate cash holding started with the seminal work by Baumol, 1952 followed by Miller and Orr, 1966. In the absence of market imperfections firms do not have transactions costs. The costs of internally generated and external funds are equal and firms can readily borrow as needed to finance investment or cover cash shortage (assuming, firms are not under financial distress). Firms, therefore, do not have any incentive to accumulate cash; rather, their cash holdings tend to react passively to cash flow. However, financial markets are far from being perfect, given asymmetric information between borrowers and lenders. Literature explaining this goes back to Keynes (1936). Firms hold cash for four reasons: first, transaction motive; second, precautionary motive to avoid any shocks due to adverse market conditions when borrowing is costly; third, tax motive, wherein multinational may keep cash in foreign subsidiaries to avoid the tax that they may incur if they repatriated the profits earned in foreign countries; finally, the agency motive, which is crucial for our research.

Jensen (1986) argued that firms with agency problems will accumulate cash if they do not have worthwhile investment opportunities and their management does not want to distribute cash to shareholders. The crucial trade-off from Jensen (1986) free cash flow hypothesis and Bates (1990) research on management’s control on financial policies is to give enough capital to managers to invest in projects with positive net present value and limit the availability of capital for projects with low cash inflow. But without a proper corporate governance (CG) mechanism in place, it is not possible to dissuade self-interested managers to invest in low return projects at the expense of returning cash to shareholders. Thus, CG plays pivotal role as it is the main mechanism by which shareholders affect managerial behaviour. CG if effectively implemented reduces agency costs and ensures managers responsibly use cash. Even in the absence of agency problems, with improvements in risk management techniques firms can hedge more effectively using derivatives, leading to lower precautionary demand for cash. But still cash holdings have gone up over the last decade. This vastness of corporate liquidity underscores the need to understand why firms accumulate cash and whether managers manage it prudently. It is pertinent to investigate whether such increase in cash results from agency problems and (or) is product of changes in firm characteristics and their business environment.

With this chapter the author aims to shed light on the role of CG in the determination of cash holding and examine how ownership structure, board and audit-related attributes (used as proxies for the nature of CG) impact cash holdings. We propose to examine how prominent India centric CG issues (like, high ownership concentration, affiliation to business groups, insiders dominating boards and single leadership structure) and specific financial determinants that influence corporate liquidity and cash. We employ four different measures of cash holdings and 24 structural indicators of CG (including, specified control variables). Using principal component analysis (PCA) we offer an exploratory inquiry into the dimensions of CG. Multiple regression is used to delve into the association between cash holdings (the dependent variable) and CG. Using a sample of 58 top listed companies, we report that firms with stronger CG tend to reduce cash balances and have higher capital expenditures, while entrenched managers with high cash reserves spend more on current assets. Firms also hold cash for financial flexibility and to take advantage of strategic opportunities as they present themselves. Parallel to this point is the fact that larger balances help firms to avoid uncertainty and hedge themselves against the difficulty of accessing external funds.

The chapter proceeds as follows: Section 2 offers a review the theoretical determinants of cash holdings. We describe our sample construction in Section 3 followed by the variables description in Section 4. Section 5 presents the research design. Section 6 gives the results of our empirical analysis followed by Section 7 that concludes.

2. Literature Review

2.1. CG and Corporate Cash Holdings

Myriad studies have focused on agency conflicts. Opler et al. (1999) argue that self-interested managers seek to accumulate cash, because they are risk averse and want flexibility to pursue personal goals. Managers have strong incentives to hold cash, since holding cash is a matter of managerial discretion, and turning excess liquidity into personal benefits is less costly to managers than transferring other assets to private benefits (Myers and Rajan, 1998). Contrary to this, Jensen (1986) argues that managers spend cash reserves, because even projects with negative net present value can increase managerial utility. For instance, acquisitions improve managers’ job security, diversify managers’ human capital risks (Morck, Shleifer & Vishny, 1990) and enhance executive compensation (Gao et al., 2013). Graham, Campbell, and Rajgopal (2005) negated the spending argument and observed that self-interested managers may hold back certain types of spending (like, research and development) because of its adverse effect on short-term earnings. However, Bates et al. (2009) offered evidence (using US firms) the inconsistency of the notion that increase in cash holdings over time can be ascribed to agency problems.

A host of internal and external CG mechanisms exists and the confluence of these factors leads to efficient CG thereby minimizing the divergent interests of the principals and agents of the firm. In this study, we use internal CG mechanism: ownership, board and audit related.

2.2. Ownership Structure

Ownership structure of firms may be important in deciding cash reserves and two contrary predictions exist. From CG perspective, ownership concentration reduces the free-rider problem, minimizes the scope of managerial opportunism and leads to mitigation of agency costs of external finance since large shareholders have more incentives to monitor management effectively. To the extent that this leads to a reduction in the cost of external finance, firms would hold less cash with higher ownership concentration. The alternative hypothesis states that large shareholders could have incentives to accumulate cash to maximize funds under their control and avoid external markets’ discipline, reduce managerial initiative, and this may lead to under diversification of such firms. As block holders, institutional investors are better in monitoring which drives firms to enhance their CG practices. Among the institutional investors, domestic mutual funds play a passive role. Banks and insurance companies tend to be more active. They also have nominee directors on the boards of the companies they invest in. Foreign institutional investors (FII) tend to exercise their ownership rights more actively. In PSE, government is the main block holder but the ultimate owners are the citizens. Gugler, Mueller and Yurtoglu (2003) pointed out the existence of double principal-agent problem in PSE. But PSE work in key economic sectors and have strong market positions. These favourable conditions create attractive investment opportunities that offset the inefficiencies created by the agency problem.

2.3. Board Structure

The effectiveness of the board in monitoring has focused on two attributes: size and composition. The influence of board size has two competing effects: more efficient and effective decision making of a smaller board against the greater monitoring by bigger boards. Larger boards trades-off added monitoring services with freeriding and will be best when managers’ opportunities to consume private benefits are high (Boone, Field, Karpoff & Raheja, 2006). Boards are often passive because they may be friendly with management. This motivates the need to employ outsiders on the board. Outside directors are those who do not have a family (or business) relationship with the company’s management. John and Senbet (1998) suggested that boards are more independent as the proportion of their outsider directors’ increases. Literature on the effect of outside directors on the agency problem is contradictory by nature. While some studies find that outside directors align with the interests of managers and shareholders, others suggest against having more outside directors. Bhagat and Black (2002) found no association between the proportion of outsider directors and firm performance. In contrast, Brickley, Coles, and Terry (1994) showed that the market rewards firms for appointing outside directors, and Anderson, Mansi, and Reeb (2004) showed the inverse relationship between cost of debt and board independence. Evidence reveals boards are becoming increasingly independent and their monitoring effectiveness has increased thereby decreasing managerial opportunism. Extensive literature exists on the separation of CEO and chairman of the board, positing that agency problems are higher when the same person holds both positions. Though duality creates a strong leadership it reduces the effectiveness of monitoring by the board.

2.4. Board Committees and Audit Related

Board committees are entrusted with complex or specialized issues and they help the board use its time more efficiently and make recommendations for action, although the board keeps collective responsibility for decision making. The audit committee is the foundation for effective CG. It has specific responsibilities in respect of the external auditors, including recommending the appointment and removal of the external auditor, approving fees paid for audit and non-audit services, and agreeing on the terms of engagement with the external auditor. Audit committee independence is crucial as the monitoring they offer affects audit quality, auditor independence (Abbott, Park, & Parker, 2000), higher disclosure quality (Karamanou & Vafeas, 2005) and lowers cost of debt (Anderson et al., 2004). Brown and Caylor (2004) offered evidence on the association between audit-related CG factors and firm performance. They saw solely independent audit committees are positively related to dividend yield but not to operating performance or firm valuation and consulting fees paid less than audit fees paid to auditors are negatively related to performance. Agrawal and Chadha (2005) found no relationship between either audit committee independence and the extent of non-audit services offered by the auditor with the probability that the firm restates its earnings. Krishnan and Visvanathan (2009) showed that firms with higher quality CG (with a financial expert on the audit committee) have lesser audit fees. In the context of auditor’s remuneration, the extent of non-audit fees collected is an area of concern because first, auditors may not stand up to management as they wish to keep the added income from non-audit fees which is management’s gift. Second, given the range of services, it may lead the auditor to identify closely with management and lose scepticism (Beattie, Fearnley, & Hines, 2002).

3. Sample Construct

We sourced our data from the Annual Reports of the firms and the Prowess database. The Prowess database is supported by the Centre for Monitoring Indian Economy and includes information on private and listed companies. It is widely used for firm-level research on India (e.g., Gopalan, Nanda & Seru, 2007).

India has two major stock exchanges: Bombay Stock Exchange of India (BSE) and National Stock Exchange of India (NSE). The BSE was established in 1875 and is Asia’s first stock exchange. It is the world’s 11th largest stock exchange with an overall market capitalisation of $1.83 trillion as of March 2017. More than 5,500 companies are publicly listed on the BSE. The NSE was founded in 1992 and started trading in 1994, as the first demutualised electronic exchange in India. The NSE has a total market capitalisation of more than US$1.41 trillion, making it the world’s 12th largest stock exchange as of March 2016. Our sample has been drawn from firms listed on the BSE’s S&P BSE 100 and NSE’s CNX 100 indexes as on 31 March 2013.

We constructed our panel data set for this study considering the period from 2007–2008 to 2012–2013, that is, six years. Our initial sample comprise of the set of all firms for which data are available with respect to the selected variables. This gave us a sample of 69 firms. We have excluded banks and financial firms (11 companies) because they carry cash to meet capital requirements and use different accounting policies and practices which may lead to non-uniformity in the computation of accounting ratios used as variables in this study. Further, we dropped missing firm-year observations for any variable in the model during the sample period. These criteria have provided us with a total of 58 companies. Our sample is well diversified comprising of firms from 22 industry groups. While 49 companies are from the manufacturing, mining, and oil and gas sectors, the balance nine are from the service sector (comprising of five software, three telecommunications and one hotel companies, respectively). There are three companies which are diversified in their nature of business. There are eight PSE included in our sample.

The Indian corporate scenario is a mix of government owned and private firms (including, family firms, corporate groups, multinationals and professionally managed standalones) and high degree of ownership concentration is common. Corporate group comprises of parent and subsidiary companies that run as a single economic entity through a common source of control. We have classified firms as a business group affiliate (BGA) based on Prowess. The Prowess database distinguishes between: (i) independent private domestic owned firms not affiliated to business groups, (ii) firms affiliated with domestic business group, (iii) PSE, (iv) independent foreign firms and (v) group-affiliated foreign firms. This classification of firms is based on continuous monitoring of company announcements and a qualitative understanding of the group-wise behaviour of individual companies. Our sample consists of 31 BGA (i.e., 53% of the sample) and the balance 27 is non-BGA (i.e., 47% of the sample) firms. There are eight firms from the Tata group, five from the Aditya Birla group, four from the Reliance (Anil Ambani) and one from Reliance (Mukesh Ambani) group. The other prominent groups include O. P. Jindal (two firms) and Mahindra and Mahindra (two firms). Individuals have controlling stake in two firms (Biocon and Divi’s Lab).

4. Variable Description

4.1. Cash Holdings

Several alternative definitions of the cash holdings exist in literature and we employ the following four measures: (i) cash to total assets, (ii) cash to net assets, (iii) current assets to total assets and (iv) current assets to net assets. For our analysis, we view cash as a liquid investment necessary to support the working capital needs. CCE includes cash and short-term, highly liquid investments that are readily convertible into cash and are subject to insignificant risk of changes in value. We have used current assets as the measure of corporate liquidity. By subtracting cash from the book value of total of all assets, we get net assets. Cash and current assets scaled by net assets have been used as a measure of how effectively they are employed and utilized by the firm.

4.2. Corporate Governance

We use several CG measures to gauge the severity of the firm’s agency problems classified into three categories: ownership structure, board structure and board committees and audit related.

4.2.1. Ownership Structure

We consider key variables like, promoter group holding, the effect of BGA (given that insider control in the Indian corporate sector is a dominant feature), institutional ownership (including FII) and PSE. Indian companies have retained their shareholding pattern over the sampled period of our study and this helped in our effort to collect data on the ownership variables. We extracted shareholding data by the promoter group and institutional holding. Institutional ownership was calculated as the sum of foreign and domestic institutional ownership. We designed a BGA dummy to check the impact of group affiliation. Similarly, to understand the influence of FII and PSE we have considered them as dummy variables.

4.2.2. Board Structure

We use board size and the proportion of outside directors, as proxy for board independence. Board consists of executive and non-executive directors. Non-executive directors play an advisory role in board meetings and deal with decision control which in turn leads to lower agency cost. A recognized best practice is that a firm should have more non-executive than executive directors. Thus, we use the proportion of non-executive directors as a variable. The decision support system of modern boards forms various board committees and they play a pivotal role in management.

4.2.3. Board Committees and Auditor’s Remuneration

In this research, we use number of such committees constituted by the firm and the existence of nomination and corporate social responsibility (CSR) committees as CG attributes. The nomination committee monitors the board and senior executive performance, succession planning and the company’s diversity policy. The CSR committee takes up policy formulation, watching of CSR activities and reviewing CSR performance. We have considered firms that have appointed CSR committee and then used that as a proxy for the level of CSR embedded in the organization.

Constitution of the audit committee, auditor’s independence, fees for services offered are important CG mechanism. We have considered the size of the audit committee and the independent directors who form of a part of it. Formal meetings of the audit committee are the heart of its work. Crucial aspects (like, the financial and risk management policies, appointment of auditors) are reviewed in audit committee meetings. There should be as many meetings as the audit committee’s role and responsibilities need and thus we have considered it as another variable.

To decide the right audit fee, auditors assess the risk associated with the client, firm size and complexity. The nature of the services offered are determinants of the audit and non-audit fees charged by the auditor. Non-audit fees include consulting services (such as systems design, taxation advice). Such fees have risen significantly (compared to audit fees) and this has led to beliefs that it can cause the auditors to compromise their independence. Thus, we have used audit fees scaled by total fees paid to the auditors as a measure in this study.

4.3. Financial and Control Variables

The other variables that we use are motivated by the transaction and precautionary explanations for cash holdings discussed previously. We use the debt equity ratio as measure of leverage and financing mix used by the firm and its financial risk. If debt is a constraint, firms will use cash to reduce leverage and lower default risk resulting in a negative relation between cash holdings and leverage. The hedging argument is consistent with a positive relation between leverage and cash holdings (Acharya, Almeida, & Campello, 2007).

Capital expenditure is likely to be positively related to cash holdings. Firms with better investment opportunities value cash more since it is costly for these firms to be financially constrained. Riddick and Whited (2009) observed a productivity shock that increases investment can lead firms to temporarily invest more and save less cash, which would lead to a lower level of cash. The pecking order theory shows large capital expenditure drains out the cash of a firm. Further, if capital expenditure creates assets that is available as collateral, it could increase debt capacity and reduce cash requirement. We use capital expenditure scaled by non-current assets (total assets less current assets) as the relevant variable. Firms with higher cash flow accumulate more cash. Following literature, we use cash flow, defined as Profit before Depreciation, Interest, Taxes and Amortization scaled by Total Assets.

We have used two measures of firm performance: (i) return on asset (ROA) and (ii) market to book value ratio (MTBVR). ROA, an accounting-based performance measure, is robust and it does not suffer from any anticipation problem. However, it suffers from an inherent bias due to historical valuation of assets. Using ROA may create problems if uniform accounting standards are not adopted. This problem does not arise in our study as India has a stable accounting regime and auditors are particularly watchful in this regard (Roy, 2016). In the context of CG, accounting measures have the potential problem of needing a longer period to capture and reflect the effects of CG as compared to market-based measures. MTBVR is a market-based performance measure. It is a forward-looking indicator of investment opportunities as it incorporates both current information and future prospects, and as such is likely to reflect better on the overall financial health of the company. It reflects firm valuation by a large universe of independent shareholders.

Firms that pay dividends are likely to be less risky and have greater access to capital markets; therefore, the precautionary motive for cash holdings is weaker for them. We include a dividend dummy and is assigned as one in years in which a firm pays a dividend, otherwise, zero. Larger firms reserve more cash to support its superior level and quality of operations and investment chances. A divergent view exists and it is based on the trade-off theory. According to it, as large firms benefit from economies of scale and have easier access of financing with lower costs they may hold lower cash. Firm size is positively related to cash holding as showed by Kalcheva and Lins (2007). Firm size also affects the composition of the board, directors’ independences, the audit process and structure of the board committees. Older firms have a longer history in capital market, years of successful operations which enhance their goodwill and have less information asymmetry relative to their newer counterparts. Therefore, they can better reach optimal cash position and continued investments, which allowed them to survive (Faulkender and Wang, 2006). Bates et al. (2009) observed that cash holdings do not increase for older firms and established firms that pay dividends. But firms that do not pay dividends increase their cash holdings dramatically. Table A1 gives the complete list of variables.

5. Research Design

We begin with univariate analysis of our sample firms and present key descriptive statistics followed by correlations. Measurement of CG is critical to this study. The concept of CG is abstract rather than concrete and observable, hence constructing an index, and using it as a proxy for this vague concept using observable measures is inappropriate. The fit between the observable ‘construct’ (CG index) and the underlying concept of CG is known as construct validity (Black, De Carvalho, Khanna, Kim, & Yurtoglu, 2017). Given CG is ‘complex construct’ (Larcker, Richardson, & Tuna, 2007) and measuring it using a single factor or an index may not be appropriate, we use PCA. PCA consists of finding clusters or components of related variables. Each component consists of a group of variables that correlate substantially among themselves than with other elements not belonging to that component. The variables are aggregated based on their statistical properties rather than on theoretical assumption or prior empirical evidence. Thus, unlike in index construction the weighting scheme is statistical instead of using equal or arbitrary weights. An area of concern, using PCA, is that the principal components are derived by eigenvalue decomposition of the correlation matrix, and the accuracy of the correlation matrix decides the validity of the principal components, and this in turn decides the reliability of the conclusions. If the variables used follow a continuous distribution, the Pearson correlations estimated are proper for PCA. But CG variables may be discrete. Pearson’s correlation coefficients tend to be underestimated for pairs of variables which include discrete data (Beekes, Hong & Owen, 2010). However, we use PCA as it offers valuable insight into the CG structure of the firm given there is little prior theory or empirical analysis about the dimensions of CG using this tool (Dey, 2008). We also perform regression analysis to examine the relation between CG and cash holdings.

6. Empirical Results

In Table A2 we have presented the summary statistics for CG and firm-specific variables. The cash and financial ratios are winsorized at the 1% level on either tail to mitigate the influence of outliers on the results. In our sampled firms, the year-on-year percentage growth in average cash holding ranges between 5.66% and 18.62%, over the sampled period. While presenting our descriptive statistics we have classified our sampled firms on the line of BGA. The mean value of CCE scaled by total asset is 19.9% in BGA firms and 19.3% in non-BGA firms. BGA firms have smaller boards with an average of 12 directors (14 in non-BGA) with about 54% (44% in non-BGA) independents directors. In addition, 68% of BGA (63% of non-BGA) firms have single leadership structure. The proportion of audit fees to auditor’s total remuneration is higher for affiliated firms at 72% (compared to 65% in non-BGA firms). A notable observation is the absence of any skewness in the CG variables as the mean and median values are close to each other. Moving to the financial variables the average cash flow multiple is 0.14 in BGA (0.15 in non-BGA) firms. The average debt equity is 0.49 in BGA (0.41 in non-BGA) with and average ROA of 10% (13% in non-BGA) and MTBVR of 3.9 in BGA (4.8 in non-BGA) firms, respectively. Finally, 94% of the BGA (93% in non-BGA) firms in the sample have distributed dividends to their shareholders.

In Table A3 we have presented the correlation coefficients evaluating the relationship between cash holdings and CG and other variables.

We employ PCA analysis to measure firm-level CG. Exploratory factor analysis output includes Kaiser–Meyer–Olkin Measure of Sampling Adequacy (KMO) and Bartlett’s Test of Sphericity. KMO is the measure of whether the distribution of values is adequate for conducting factor analysis. It yields a KMO of 0.653, which is middling. The KMO value should be greater than 0.5 for sampling adequacy to hold. But for a complex construct like CG, where not much existing literature is available using this tool, it is difficult to set a standard and so this may be considered adequate. Bartlett’s Test of Sphericity is the measure of the multivariate normality of the set of distributions. In this study, the significance value is zero (<0.05) showing that the data do not produce an identity matrix and are multivariate normal, hence acceptable. According to Field (2009) the value of the diagonal elements of the anti-image correlation matrix for all variables should be greater than or equal to 0.5. Variables with values below 0.5 have been excluded from our analysis. We keep all factors with an Eigen value greater than unity. This results in seven factors that keep 69.84% of the total variance in the original data, and these seven factors characterize the dimensionality of our 24 individual indicators. We rotated the reduced solution using ‘varimax’ rotation that allows the retained factors to be correlated to enhance interpretability of the PCA solution. To interpret the factors, it is necessary to decide which indicators have a statistical and substantive association with each factor. We associate each factor with those variables that have a loading that exceeds 0.40 in absolute value and are different (statistically) from zero at conventional levels. The PCA results produce an interpretable solution. There exist a few cross-loadings where the same indicator is associated with more than one factor. We believe since CG is a ‘complex construct’, it is not surprising to find some unexpected results in the PCA solution.

In Table A4 we have summarized the variables associated with each factor. CGF#1 has six variable loadings out of which five are positive and one attribute is negative. This means that the negative variable decreases in size when the other five increase in size together. These six components explain 14.53% of variance. CGF#2 has four loadings of which three are positive and one negative showing that this negative variable decreases in size when the other three increase in size together. These four components explain 11.45% of variance. We interpret the other factors in an analogous way. The interpretation of these loadings has content validity as a measure of CG and the selected variables.

There is a need for the scale reliability of those dimensions that make up the factors; hence we compute Cronbach’s α. Cronbach’s α is a measure of the correlation between elements of a multipart measure that ranges from 0 to 1. The mean (median) of the alpha coefficient is 0.51 (0.65). This level of reliability is lower than the benchmarks suggested by Nunnally (1967). Despite this we feel our measurement analysis has higher level of reliability than single indicators used to measure CG.

Our aim was to understand the cash holdings and CG. The dependent variables are cash holding ratios. The independent variables are CG and firm-specific variables affecting cash. Assuming a linear relationship exists between them, we examine whether there exists is an association. Our objective functions are:

  • CCE/TA = f (Ownership, Board structure, Audit related, Financial & control variables, error)

  • CCE/NA = f (Ownership, Board structure, Audit related, Financial & control variables, error)

  • CA/TA = f (Ownership, Board structure, Audit related, Financial & control variables, error)

  • CA/NA = f (Ownership, Board structure, Audit related, Financial & control variables, error)

Using the factor scores generated by PCA we run regression. Models I and II yielded R-square values of 21.4% and 17.7%, respectively. This signifies that 21.4% and 17.7% of the variability in cash holding are accounted for by the models considering the predictor variables. In both models I and II, two factors turn out to be significant: CGF#2 is positively related while CGF#7 is negatively related to cash holdings, respectively. Models III and IV yielded R-square values of 47.3% and 45%, respectively. In model III, we document a negative relationship between CG factors, CGF#6 and CGF#7 and firm liquidity. In model IV, the result suggests a negative relationship between CGF#7 and firm liquidity.

The coefficients of the CG variables explain the predictions of our research problem relating CG to cash ratios. A significant negative coefficient shows that firms with stronger CG tend to reduce cash balances and have higher capital expenditures, while entrenched managers with high cash reserves spend more on current assets. A significant positive coefficient suggests that management accumulates excess cash if it can do so and the motivation for this behaviour seems to be that the precautionary motive for holding cash is strong. In emerging markets, like India, firms hold cash or own liquidity for financial flexibility compared to developed markets where such cash holding is independent of the CG structure. Firms hoard cash to take advantage of strategic opportunities as they present themselves. Firms are expecting higher growth opportunities as compared to advanced economies, which are saturated markets. Parallel to this point is the fact that larger cash reserves help in avoiding uncertainty and hedge against the difficulty of accessing external funds possibly because of limited collaterals and economies of scale. The results are presented in Table A5.

7. Conclusion

This is an exploratory study to understand the relationship between CG and cash holdings. We assembled a detailed data set comprising of 58 listed companies, considering data for six years, and with all firm year data available on the selected variables. Our empirical analysis on CG and cash in an emerging economy, like India, has sought to add to the limited evidence that exists on this issue. The relevance of this study is highlighted by the fact that there has been persistent and increasing pattern in cash holdings, at a time when governance among Indian corporates was emerging as a force to recon with. We studied three key internal aspects of CG: namely, ownership, board characteristics and audit related.

Our set of companies being large (and some are diversified), profitable businesses have generated enough and more from their operations that enabled them to save up. The growth in cash reserve has been across all firms, including the service sector companies. This indicates that they have resources available for acquisitions, since these companies primarily resort to inorganic growth. Further, PSE have also piled up cash and they may be encouraged to invest in and improve the infrastructure facilities that act as an impediment to business growth.

We undertook a comprehensive analysis of the CG structures to develop a measure of CG and related those measures to corporate cash holdings. In order to identify the structure of CG, we used PCA. We derived seven factors from 24 individual CG indicators and selected financial and control variables. We controlled for cash flow, leverage, capital expenditure, profitability, growth, dividend pay-out, firm age and size. Using multiple regression, we report that our CG constructs have an association with the measures of corporate cash holdings and liquidity used in this study. Based on the signs of the estimated coefficients we found that the derived CG constructs are related to cash holdings but have a somewhat mixed association. Firms with stronger CG tend to have reduced cash balances and higher capital budgets, while entrenched managers in firms with high cash reserves tend to invest more in current assets. On the other hand, in firms where the CG structures are not that robust, management may have accumulated excess cash possibly because the precautionary motive was stronger. Larger cash reserves helped these firms to avoid uncertainty and offered hedge against the difficulty of accessing external funds. It could also be that firms with weaker CG structures held low cash reserves to mitigate the potential agency problems associated with excess cash holdings. Further, Indian firms have shown the liking for holding cash for financial flexibility and to take advantage of higher growth opportunities as compared to advanced economies, which are saturated markets. However, during our study period the Indian economy did exhibit signs of slowing down and this could have led to firms putting their investment plans on hold but with their continued growth in profits, cash surpluses were inevitable.

As with all studies, it is important to identify and state the limitations of our research. The CG variables used arise out of the regulatory mechanisms that exist in India. If these regulations caused firms to adopt greater conformity in their CG structure, then it may reduce the cross-sectional variation in our selected variables and decrease the power of our statistical inference. However, India has a stable regulatory environment and our analysis of the related CG variables does not indicate substantial changes. Thus, we believe that our study has sufficient power to detect the association between CG and cash holdings. Second, CG characteristics are endogenous variables and have the potential of creating econometric problems. We mitigate this by using the standard approach adopted in literatures that suggest the use of predictor variables (e.g., firm size). This approach does not completely resolve endogeneity. Third, our sample size was restricted. This is due to the hand picking of many variables from the annual reports of the companies. An extension of this study may be considered using a larger data set to verify if our conclusions can be made more robust.

Our contribution has been the consistent estimation of the relationship between CG and cash holding and liquidity, considering the inter-relationships among CG and financial variables in listed Indian firms. We believe that this relationship is of importance to corporates, investors, academics and policy makers.

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Appendix

Table A1.

List of Corporate Governance Attributes and Other Variables.

Variable Definition
Ownership structure
 1. PGH Proportion of shares held by promoter group
 2. INSTH Proportion of shares held by institutional investors
 3. BGA Business group affiliate, dummy variable, if the firm is affiliated to any business group (as identified by Prowess) then one, and zero otherwise
 4. PSE Public sector enterprise, dummy variable, if the firm is a PSE then one, and zero otherwise
 5. FII Foreign Institutional Investment, dummy variable, if the firm has attracted FII then one, and zero otherwise
Board structure
 6. BS Number of directors on the Board
 7. PropID Proportion of independent directors on Board
 8. Prop_NED Proportion of non-executive directors on Board
 9. CEODual Dummy variable, this variable is assigned the value of zero where the CEO has the dual role of Chairman of the Board as well as CEO, and one otherwise
10. No._BdCom Number of Board Committees
11. CSRCom Dummy variable, this variable is assigned a value of one if the firm has a Corporate Social Responsibility or Sustainability Committee, and zero otherwise
12. NomCom Dummy variable, this variable is assigned a value of one if the firm as a Nomination Committee, and zero otherwise
Audit related
13. AdCom_Sz Audit committee size
14. IDinAC Number of independent directors on the audit committee
15. AdCom_Mt Number of meetings held by the audit committee
16. Afee_Totfee Audit fees scaled by total remuneration paid to auditors
Financial and control variables
17. DE Debt-equity ratio
18. CF_PBDITA/TA Cash flow generated defined as profit before depreciation, interest, taxes and amortization scaled by total assets
19. Capex/NCA Capital expenditure scaled by non-current asset
20. ROA Return on assets
21. MTBVR Market to book value ratio
22. DivDum Dummy variable, this variable is assigned a value of 1 if the company has paid dividend in a given year, and 0 otherwise
23. Ln_TA Natural logarithm of total assets of the firm as an indicator of Firm Size
24. Ln_AGE Natural logarithm of firm age since incorporation
Cash holding ratios
25. CCE/TA Cash and cash equivalents scaled by total assets
26. CCE/NA Cash and cash equivalents scaled by net assets
27. CA/TA Current asset scaled by total assets
28. CA/NA Current asset scaled by net assets

Note: The table reports the 16 individual governance attributes grouped by the three sub-categories: ownership, board and audit related along with financial and control variables.

Table A2

Descriptive Statistics.

Panel A
Firm Classification Unit BGA Non-BGA
Mean Median Max Min SD Mean Median Max Min SD
CG and firm-specific variables
 1. Proportion of shares held by promoter group % 0.491 0.50 0.804 0.253 0.16 0.548 0.551 0.9 0 0.231
 2. Proportion of shares held by institutional investors % 0.304 0.284 0.546 0.066 0.12 0.28 0.267 0.543 0.074 0.13
 3. Board size Persons 12 13 17 5 3.41 14 13 22 8 4.07
 4. Proportion of ID on the board % 0.54 0.52 0.8 0.35 0.1 0.44 0.44 0.66 0.17 0.11
 5. Proportion of NED on the board % 0.7 0.74 0.91 0.2 0.15 0.59 0.57 0.92 0.35 0.16
 6. No. of board committees Number 6 5 11 2 2.06 7 6 16 2 3.66
 7. Audit committee size Persons 7 7 10 4 1.7 10 9 18 4 4.15
 8. No. of audit committee meetings held Number 6 6 12 3 1.78 6 6 10 4 1.76
 9. ID on audit committee Persons 5 5 8 3 1.36 8 7 16 3 4.19
10. Audit fee scaled by auditor’s total remuneration % 0.72 0.7 0.99 0.36 0.16 0.65 0.73 0.96 0.22 0.22
11. Debt-equity ratio Multiple 0.49 0.37 1.69 0.01 0.4 0.41 0.09 2.23 0 0.65
12. CF_PBDITAtoTA Multiple 0.14 0.13 0.33 0 0.08 0.15 0.15 0.31 0 0.09
13. Firm age Years 42 33 110 5 30 44 39 102 6 23
14. Capex/NCA Multiple 0.095 0.058 0.512 −0.177 0.134 0.061 0.053 0.187 0.000 0.048
15. Total assets Rs. millions 266,679 131,245 2,454,954 17,124 452,089 360,856 259,934 1,645,891 39,784 367,218
16. ROA % 0.1 0.07 0.3 −0.04 0.08 0.13 0.11 0.62 −0.08 0.13
17. MTBVR Multiple 3.9 2.95 11.72 0.97 2.66 4.82 3.27 23.97 1.71 4.37
Cash holding ratios
18. CCE/TA Multiple 0.199 0.149 0.954 0.019 0.185 0.193 0.151 0.532 0.036 0.126
19. CCE/NA Multiple 0.331 0.177 2.376 0.020 0.459 0.286 0.182 1.250 0.038 0.273
20. CA/TA Multiple 0.577 0.592 0.995 −0.295 0.273 0.634 0.672 1.000 0.178 0.254
21. CA/NA Multiple 0.785 0.822 2.108 −0.371 0.46 0.835 0.778 2.039 0.212 0.43
Panel B
Firm classification BGA Non-BGA
Other CG and firm-specific variables No. of firms Proportion No. of firms Proportion
1. FII 29 0.94 21 0.78
2. CEO duality 21 0.68 17 0.63
3. CSR/sustainability committee  6 0.19  9 0.33
4. Nomination committee 18 0.58  5 0.19
5. Dividends declared 29 0.94 25 0.93

Notes: Panel A – The descriptive statistics in respect of the CG and firm-specific variables. Firms are having been classified as those having business group affiliated and non-affiliated firms.

Panel B – Firms are classified as those having business group affiliated (total no. of firms 31) and non-affiliated firms (total no. of firms 27).

Table A3

Correlations Matrix.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
 1. CCE/TA 1 0.976** 0.130 0.664** −0.072 0.143 0.021 −0.046 0.022 −0.077 −0.053 −0.129 0.189 0.085 0.081 −0.086 −0.138 −0.031 −0.052 0.082 −0.249 0.086 0.336** 0.162 0.296* 0.181 −0.178 0.067
0 0.332 0.000 0.593 0.285 0.874 0.732 0.870 0.568 0.695 0.333 0.155 0.524 0.546 0.521 0.301 0.820 0.696 0.539 0.059 0.521 0.010 0.226 0.024 0.175 0.181 0.615
 2. CCE/NA 1 0.130 0.680** −0.110 0.173 0.060 −0.004 −0.062 −0.078 −0.038 −0.108 0.175 0.116 0.089 −0.081 −0.133 −0.016 −0.050 0.093 −0.187 0.030 0.280* 0.143 −0.315* 0.167 −0.166 0.043
0.331 0.000 0.410 0.195 0.656 0.978 0.645 0.563 0.775 0.418 0.189 0.388 0.509 0.544 0.321 0.904 0.711 0.488 0.161 0.821 0.033 0.285 0.016 0.210 0.214 0.748
 3. CA/TA 1 0.807** −0.047 0.015 −0.109 −0.204 −0.151 0.347** 0.081 −0.006 0.207 −0.143 0.139 −0.038 −0.052 −0.208 0.042 −0.094 −0.106 0.054 −0.198 −0.171 0.646** 0.198 −0.069 −0.115
0.000 0.724 0.914 0.417 0.124 0.258 0.008 0.547 0.966 0.119 0.284 0.299 0.775 0.697 0.117 0.755 0.484 0.428 0.690 0.136 0.199 0.000 0.135 0.605 0.392
 4. CA/NA 1 −0.082 0.087 −0.056 −0.150 −0.137 0.312* 0.026 −0.097 0.257 0.001 0.143 −0.053 −0.092 −0.186 −0.011 −0.009 −0.187 0.049 0.030 −0.036 0.641** 0.236 −0.136 −0.079
0.541 0.515 0.678 0.261 0.307 0.017 0.848 0.470 0.052 0.993 0.285 0.694 0.492 0.161 0.934 0.948 0.160 0.715 0.825 0.786 0.000 0.075 0.310 0.556
 5. PGH 1 0.887** −0.146 0.426** 0.425** −0.106 0.063 −0.133 0.292* 0.221 −0.134 0.330* 0.323* −0.169 −0.085 0.098 −0.005 0.053 0.041 −0.215 0.143 0.328* 0.048 −0.141
0.000 0.274 0.001 0.001 0.428 0.639 0.318 0.026 0.095 0.317 0.011 0.013 0.204 0.527 0.464 0.972 0.692 0.758 0.105 0.283 0.012 0.720 0.290
 6. INSTH 1 0.101 0.473** 0.310* 0.129 −0.056 0.101 0.255 −0.132 0.088 0.286* 0.298* 0.236 0.083 −0.072 −0.021 −0.029 0.088 0.252 −0.088 0.366** −0.050 0.295*
0.453 0.000 0.018 0.335 0.677 0.450 0.053 0.322 0.512 0.029 0.023 0.074 0.535 0.589 0.874 0.827 0.513 0.056 0.512 0.005 0.711 0.024
 7. BGA 1 0.228 0.429** −0.253 0.412** 0.329* 0.011 −0.196 0.403** 0.457** 0.416** −0.021 0.161 −0.159 0.135 −0.131 −0.043 −0.031 0.166 −0.116 0.285* 0.019
0.085 0.001 0.055 0.001 0.012 0.932 0.139 0.002 0.000 0.001 0.875 0.228 0.232 0.312 0.328 0.750 0.820 0.213 0.386 0.030 0.889
 8. FII 1 0.275* 0.007 −0.015 0.208 0.147 0.058 0.018 −0.084 −0.018 0.045 −0.043 0.008 0.088 0.081 0.151 0.298* 0.181 0.091 −0.189 0.286*
0.037 0.960 0.912 0.118 0.271 0.663 0.896 0.532 0.896 0.739 0.750 0.953 0.512 0.546 0.257 0.023 0.174 0.498 0.155 0.030
 9. PSE 1 0.344** 0.324* 0.381** 0.360** 0.455** −0.324* 0.563** 0.571** 0.281* 0.401** 0.449** 0.082 −0.149 −0.116 −0.087 −0.076 −0.008 0.460** 0.109
0.008 0.013 0.003 0.006 0.000 0.013 0.000 0.000 0.032 0.002 0.000 0.539 0.264 0.388 0.515 0.573 0.952 0.000 0.416
10. BS 1 0.490** −0.060 −0.084 0.088 −0.324* 0.401** 0.401** 0.260* −0.248 0.136 0.156 0.049 0.058 0.310* 0.195 0.146 0.293* 0.242
0.000 0.652 0.532 0.510 0.013 0.002 0.002 0.049 0.060 0.308 0.244 0.715 0.663 0.018 0.142 0.274 0.026 0.068
11. PropID 1 0.092 −0.005 −0.060 0.463** −0.213 −0.188 −0.223 0.357** −0.006 0.095 −0.078 −0.030 −0.153 −0.022 0.301* −0.073 −0.127
0.493 0.968 0.653 0.000 0.108 0.158 0.092 0.006 0.964 0.478 0.560 0.823 0.251 0.871 0.022 0.584 0.340
12. PropNED 1 0.244 0.008 0.215 −0.121 −0.116 0.010 0.124 −0.076 −0.077 0.059 0.109 0.137 0.124 0.074 0.389** −0.017
0.065 0.950 0.105 0.365 0.387 0.939 0.355 0.573 0.564 0.661 0.414 0.304 0.354 0.579 0.003 0.896
13. CEODual 1 0.151 0.115 −0.204 −0.220 0.070 0.092 −0.007 0.002 0.080 0.037 −0.060 −0.159 0.171 −0.183 −0.045
0.259 0.389 0.125 0.097 0.602 0.493 0.957 0.985 0.552 0.782 0.653 0.234 0.199 0.169 0.737
14. No._BrdCm 1 0.082 0.406** 0.390** 0.314* 0.276* 0.623** −0.082 −0.126 0.132 −0.031 0.019 0.031 0.240 0.028
0.540 0.002 0.002 0.017 0.036 0.000 0.540 0.345 0.323 0.818 0.885 0.816 0.070 0.834
15. NomCom 1 −0.229 −0.215 0.035 0.192 0.004 −0.079 −0.056 0.010 −0.084 −0.071 −0.153 −0.127 −0.058
0.084 0.105 0.796 0.148 0.975 0.554 0.675 0.940 0.531 0.594 0.251 0.343 .668
16. IDinAC 1 .961** 0.004 0.337** 0.410** 0.165 −0.017 −0.083 −0.056 −0.001 −0.044 0.414** −0.138
0.000 0.979 0.010 0.001 0.214 0.901 0.538 0.674 0.993 0.740 0.001 0.300
17. AudCom_Sz 1 0.000 0.340** 0.430** 0.183 0.028 −0.118 −0.038 0.009 −0.061 0.417** −0.093
0.999 0.009 0.001 0.169 0.833 0.376 0.779 0.949 0.648 0.001 0.486
18. AdCm _Mtng 1 −0.174 0.230 0.047 −0.135 −0.179 −0.083 −0.028 0.335* 0.348** 0.158
0.192 0.082 0.724 0.313 0.180 0.536 0.832 0.010 0.007 0.237
19. AF/TF 1 0.313* 0.045 0.033 −0.020 −0.130 0.026 −0.074 −0.234 −0.191
0.017 0.736 0.805 0.879 0.329 0.845 0.580 0.077 0.151
20. CSR_Com 1 −0.025 −0.129 −0.120 −0.095 −0.129 0.008 0.336** −0.150
0.850 0.334 0.370 0.476 0.334 0.954 0.010 0.261
21. DE 1 0.260* 0.505** 0.426** 0.016 0.000 0.239 −0.063
0.049 0.000 0.001 0.903 0.999 0.071 0.638
22. MTBVR 1 0.538** 0.417** 0.115 0.043 0.399** 0.100
0.000 0.001 0.392 0.750 0.002 0.453
23. ROA 1 0.634** 0.298* −0.065 0.437** 0.383**
0.000 0.023 0.627 0.001 0.003
24. CF 1 0.207 0.025 −0.251 0.301*
0.120 0.854 0.057 0.022
25. Capex/NCA 1 0.365** −0.082 0.006
0.005 0.543 0.964
26. Ln_AGE 1 0.081 0.279*
0.545 0.034
27. LogTA 1 −0.041
0.759
28. DivDum 1

Notes: The results of correlation between the variables used in this study are presented here. The correlation coefficients and their significance are disclosed.

* Correlation is significant at the 0.05 level (two-tailed).

** Correlation is significant at the 0.01 level (two-tailed)

Table A4

Rotated Component Matrix.

Aspect of CG Component
1 2 3 4 5 6 7
CGF#1 CGF#2 CGF#3 CGF#4 CGF#5 CGF#6 CGF#7
Panel A: Total variance
Variance retained (%) 14.529 11.445 11.310 11.095 7.310 7.086 7.067
Cumulative variance (%) 14.529 25.974 37.284 48.378 55.689 62.775 69.841
Panel B: Factor loadings
 1. AudCom_Size 0.856
 2. IDinAC 0.842
 3. CSR_Com 0.728
 4. No._of_BrdCom 0.718 0.446
 5. PSU 0.560 −0.406
 6. AFtoTF −0.401
 7. ROA 0.868
 8. DE −0.771
 9. CF_PBDITA/TA 0.730
10. MTBVR 0.610
11. PropID −0.793
12. NomCom −0.728
13. BS 0.662
14. BGA −0.465 0.453
15. DivDum 0.420
16. INSTH 0.891
17. PGH −0.873
18. FII 0.686
19. PropNED 0.768
20. CEODuality 0.651
21. Ln_TA 0.406 −0.452 −0.494
22. AudCom_Mtng 0.784
23. Capex/NCA 0.816
24. Ln_AGE −0.489

Notes: The results of the rotated component matrix are shown here. Extraction method: principal component analysis. The Rotation Method used is Varimax with Kaiser Normalisation. Rotation converged in 20 iterations.

Table A5

Results of Regression Analysis.

Model I II III IV
CCE/TA CCE/NA CA/TA CA/NA
CGF#1 −0.016 (0.900) −0.001 (0.992) −0.079 (0.447) −0.032 (0.758)
CGF#2 0.318 (0.015)* 0.254 (0.054)* −0.045 (0.664) 0.122 (0.251)
CGF#3 −0.081 (0.521) −0.107 (0.410) −0.147 (0.159) −0.174 (0.104)
CGF#4 0.055 (0.665) 0.101 (0.434) −0.065 (0.526) −0.004 (0.973)
CGF#5 0.003 (0.982) 0.017 (0.896) 0.070 (0.500) 0.053 (0.617)
CGF#6 0.086 (0.495) 0.088 (0.494) 0.210 (0.046)* −0.114 (0.282)
CGF#7 0.309 (0.017)* 0.288 (0.029)* 0.624 (0.000)** 0.623 (0.000)**
R2 21.4% 17.7% 47.3% 45%
Constant 9.921 (0.000) 6.397 (0.000) 22.518 (0.000) 17.545 (0.000)

Notes: The results in this table examine whether the dependent variables are influenced by the CG and financial factors derived using principal component analysis. The standardized coefficients (beta) and their significance, within parentheses, are disclosed.

* Coefficient is significant at the 5% level.

** Coefficient is significant at the 1% level.

Appendix

Table A1.

List of Corporate Governance Attributes and Other Variables.

Variable Definition
Ownership structure
 1. PGH Proportion of shares held by promoter group
 2. INSTH Proportion of shares held by institutional investors
 3. BGA Business group affiliate, dummy variable, if the firm is affiliated to any business group (as identified by Prowess) then one, and zero otherwise
 4. PSE Public sector enterprise, dummy variable, if the firm is a PSE then one, and zero otherwise
 5. FII Foreign Institutional Investment, dummy variable, if the firm has attracted FII then one, and zero otherwise
Board structure
 6. BS Number of directors on the Board
 7. PropID Proportion of independent directors on Board
 8. Prop_NED Proportion of non-executive directors on Board
 9. CEODual Dummy variable, this variable is assigned the value of zero where the CEO has the dual role of Chairman of the Board as well as CEO, and one otherwise
10. No._BdCom Number of Board Committees
11. CSRCom Dummy variable, this variable is assigned a value of one if the firm has a Corporate Social Responsibility or Sustainability Committee, and zero otherwise
12. NomCom Dummy variable, this variable is assigned a value of one if the firm as a Nomination Committee, and zero otherwise
Audit related
13. AdCom_Sz Audit committee size
14. IDinAC Number of independent directors on the audit committee
15. AdCom_Mt Number of meetings held by the audit committee
16. Afee_Totfee Audit fees scaled by total remuneration paid to auditors
Financial and control variables
17. DE Debt-equity ratio
18. CF_PBDITA/TA Cash flow generated defined as profit before depreciation, interest, taxes and amortization scaled by total assets
19. Capex/NCA Capital expenditure scaled by non-current asset
20. ROA Return on assets
21. MTBVR Market to book value ratio
22. DivDum Dummy variable, this variable is assigned a value of 1 if the company has paid dividend in a given year, and 0 otherwise
23. Ln_TA Natural logarithm of total assets of the firm as an indicator of Firm Size
24. Ln_AGE Natural logarithm of firm age since incorporation
Cash holding ratios
25. CCE/TA Cash and cash equivalents scaled by total assets
26. CCE/NA Cash and cash equivalents scaled by net assets
27. CA/TA Current asset scaled by total assets
28. CA/NA Current asset scaled by net assets

Note: The table reports the 16 individual governance attributes grouped by the three sub-categories: ownership, board and audit related along with financial and control variables.

Table A2

Descriptive Statistics.

Panel A
Firm Classification Unit BGA Non-BGA
Mean Median Max Min SD Mean Median Max Min SD
CG and firm-specific variables
 1. Proportion of shares held by promoter group % 0.491 0.50 0.804 0.253 0.16 0.548 0.551 0.9 0 0.231
 2. Proportion of shares held by institutional investors % 0.304 0.284 0.546 0.066 0.12 0.28 0.267 0.543 0.074 0.13
 3. Board size Persons 12 13 17 5 3.41 14 13 22 8 4.07
 4. Proportion of ID on the board % 0.54 0.52 0.8 0.35 0.1 0.44 0.44 0.66 0.17 0.11
 5. Proportion of NED on the board % 0.7 0.74 0.91 0.2 0.15 0.59 0.57 0.92 0.35 0.16
 6. No. of board committees Number 6 5 11 2 2.06 7 6 16 2 3.66
 7. Audit committee size Persons 7 7 10 4 1.7 10 9 18 4 4.15
 8. No. of audit committee meetings held Number 6 6 12 3 1.78 6 6 10 4 1.76
 9. ID on audit committee Persons 5 5 8 3 1.36 8 7 16 3 4.19
10. Audit fee scaled by auditor’s total remuneration % 0.72 0.7 0.99 0.36 0.16 0.65 0.73 0.96 0.22 0.22
11. Debt-equity ratio Multiple 0.49 0.37 1.69 0.01 0.4 0.41 0.09 2.23 0 0.65
12. CF_PBDITAtoTA Multiple 0.14 0.13 0.33 0 0.08 0.15 0.15 0.31 0 0.09
13. Firm age Years 42 33 110 5 30 44 39 102 6 23
14. Capex/NCA Multiple 0.095 0.058 0.512 −0.177 0.134 0.061 0.053 0.187 0.000 0.048
15. Total assets Rs. millions 266,679 131,245 2,454,954 17,124 452,089 360,856 259,934 1,645,891 39,784 367,218
16. ROA % 0.1 0.07 0.3 −0.04 0.08 0.13 0.11 0.62 −0.08 0.13
17. MTBVR Multiple 3.9 2.95 11.72 0.97 2.66 4.82 3.27 23.97 1.71 4.37
Cash holding ratios
18. CCE/TA Multiple 0.199 0.149 0.954 0.019 0.185 0.193 0.151 0.532 0.036 0.126
19. CCE/NA Multiple 0.331 0.177 2.376 0.020 0.459 0.286 0.182 1.250 0.038 0.273
20. CA/TA Multiple 0.577 0.592 0.995 −0.295 0.273 0.634 0.672 1.000 0.178 0.254
21. CA/NA Multiple 0.785 0.822 2.108 −0.371 0.46 0.835 0.778 2.039 0.212 0.43
Panel B
Firm classification BGA Non-BGA
Other CG and firm-specific variables No. of firms Proportion No. of firms Proportion
1. FII 29 0.94 21 0.78
2. CEO duality 21 0.68 17 0.63
3. CSR/sustainability committee  6 0.19  9 0.33
4. Nomination committee 18 0.58  5 0.19
5. Dividends declared 29 0.94 25 0.93

Notes: Panel A – The descriptive statistics in respect of the CG and firm-specific variables. Firms are having been classified as those having business group affiliated and non-affiliated firms.

Panel B – Firms are classified as those having business group affiliated (total no. of firms 31) and non-affiliated firms (total no. of firms 27).

Table A3

Correlations Matrix.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
 1. CCE/TA 1 0.976** 0.130 0.664** −0.072 0.143 0.021 −0.046 0.022 −0.077 −0.053 −0.129 0.189 0.085 0.081 −0.086 −0.138 −0.031 −0.052 0.082 −0.249 0.086 0.336** 0.162 0.296* 0.181 −0.178 0.067
0 0.332 0.000 0.593 0.285 0.874 0.732 0.870 0.568 0.695 0.333 0.155 0.524 0.546 0.521 0.301 0.820 0.696 0.539 0.059 0.521 0.010 0.226 0.024 0.175 0.181 0.615
 2. CCE/NA 1 0.130 0.680** −0.110 0.173 0.060 −0.004 −0.062 −0.078 −0.038 −0.108 0.175 0.116 0.089 −0.081 −0.133 −0.016 −0.050 0.093 −0.187 0.030 0.280* 0.143 −0.315* 0.167 −0.166 0.043
0.331 0.000 0.410 0.195 0.656 0.978 0.645 0.563 0.775 0.418 0.189 0.388 0.509 0.544 0.321 0.904 0.711 0.488 0.161 0.821 0.033 0.285 0.016 0.210 0.214 0.748
 3. CA/TA 1 0.807** −0.047 0.015 −0.109 −0.204 −0.151 0.347** 0.081 −0.006 0.207 −0.143 0.139 −0.038 −0.052 −0.208 0.042 −0.094 −0.106 0.054 −0.198 −0.171 0.646** 0.198 −0.069 −0.115
0.000 0.724 0.914 0.417 0.124 0.258 0.008 0.547 0.966 0.119 0.284 0.299 0.775 0.697 0.117 0.755 0.484 0.428 0.690 0.136 0.199 0.000 0.135 0.605 0.392
 4. CA/NA 1 −0.082 0.087 −0.056 −0.150 −0.137 0.312* 0.026 −0.097 0.257 0.001 0.143 −0.053 −0.092 −0.186 −0.011 −0.009 −0.187 0.049 0.030 −0.036 0.641** 0.236 −0.136 −0.079
0.541 0.515 0.678 0.261 0.307 0.017 0.848 0.470 0.052 0.993 0.285 0.694 0.492 0.161 0.934 0.948 0.160 0.715 0.825 0.786 0.000 0.075 0.310 0.556
 5. PGH 1 0.887** −0.146 0.426** 0.425** −0.106 0.063 −0.133 0.292* 0.221 −0.134 0.330* 0.323* −0.169 −0.085 0.098 −0.005 0.053 0.041 −0.215 0.143 0.328* 0.048 −0.141
0.000 0.274 0.001 0.001 0.428 0.639 0.318 0.026 0.095 0.317 0.011 0.013 0.204 0.527 0.464 0.972 0.692 0.758 0.105 0.283 0.012 0.720 0.290
 6. INSTH 1 0.101 0.473** 0.310* 0.129 −0.056 0.101 0.255 −0.132 0.088 0.286* 0.298* 0.236 0.083 −0.072 −0.021 −0.029 0.088 0.252 −0.088 0.366** −0.050 0.295*
0.453 0.000 0.018 0.335 0.677 0.450 0.053 0.322 0.512 0.029 0.023 0.074 0.535 0.589 0.874 0.827 0.513 0.056 0.512 0.005 0.711 0.024
 7. BGA 1 0.228 0.429** −0.253 0.412** 0.329* 0.011 −0.196 0.403** 0.457** 0.416** −0.021 0.161 −0.159 0.135 −0.131 −0.043 −0.031 0.166 −0.116 0.285* 0.019
0.085 0.001 0.055 0.001 0.012 0.932 0.139 0.002 0.000 0.001 0.875 0.228 0.232 0.312 0.328 0.750 0.820 0.213 0.386 0.030 0.889
 8. FII 1 0.275* 0.007 −0.015 0.208 0.147 0.058 0.018 −0.084 −0.018 0.045 −0.043 0.008 0.088 0.081 0.151 0.298* 0.181 0.091 −0.189 0.286*
0.037 0.960 0.912 0.118 0.271 0.663 0.896 0.532 0.896 0.739 0.750 0.953 0.512 0.546 0.257 0.023 0.174 0.498 0.155 0.030
 9. PSE 1 0.344** 0.324* 0.381** 0.360** 0.455** −0.324* 0.563** 0.571** 0.281* 0.401** 0.449** 0.082 −0.149 −0.116 −0.087 −0.076 −0.008 0.460** 0.109
0.008 0.013 0.003 0.006 0.000 0.013 0.000 0.000 0.032 0.002 0.000 0.539 0.264 0.388 0.515 0.573 0.952 0.000 0.416
10. BS 1 0.490** −0.060 −0.084 0.088 −0.324* 0.401** 0.401** 0.260* −0.248 0.136 0.156 0.049 0.058 0.310* 0.195 0.146 0.293* 0.242
0.000 0.652 0.532 0.510 0.013 0.002 0.002 0.049 0.060 0.308 0.244 0.715 0.663 0.018 0.142 0.274 0.026 0.068
11. PropID 1 0.092 −0.005 −0.060 0.463** −0.213 −0.188 −0.223 0.357** −0.006 0.095 −0.078 −0.030 −0.153 −0.022 0.301* −0.073 −0.127
0.493 0.968 0.653 0.000 0.108 0.158 0.092 0.006 0.964 0.478 0.560 0.823 0.251 0.871 0.022 0.584 0.340
12. PropNED 1 0.244 0.008 0.215 −0.121 −0.116 0.010 0.124 −0.076 −0.077 0.059 0.109 0.137 0.124 0.074 0.389** −0.017
0.065 0.950 0.105 0.365 0.387 0.939 0.355 0.573 0.564 0.661 0.414 0.304 0.354 0.579 0.003 0.896
13. CEODual 1 0.151 0.115 −0.204 −0.220 0.070 0.092 −0.007 0.002 0.080 0.037 −0.060 −0.159 0.171 −0.183 −0.045
0.259 0.389 0.125 0.097 0.602 0.493 0.957 0.985 0.552 0.782 0.653 0.234 0.199 0.169 0.737
14. No._BrdCm 1 0.082 0.406** 0.390** 0.314* 0.276* 0.623** −0.082 −0.126 0.132 −0.031 0.019 0.031 0.240 0.028
0.540 0.002 0.002 0.017 0.036 0.000 0.540 0.345 0.323 0.818 0.885 0.816 0.070 0.834
15. NomCom 1 −0.229 −0.215 0.035 0.192 0.004 −0.079 −0.056 0.010 −0.084 −0.071 −0.153 −0.127 −0.058
0.084 0.105 0.796 0.148 0.975 0.554 0.675 0.940 0.531 0.594 0.251 0.343 .668
16. IDinAC 1 .961** 0.004 0.337** 0.410** 0.165 −0.017 −0.083 −0.056 −0.001 −0.044 0.414** −0.138
0.000 0.979 0.010 0.001 0.214 0.901 0.538 0.674 0.993 0.740 0.001 0.300
17. AudCom_Sz 1 0.000 0.340** 0.430** 0.183 0.028 −0.118 −0.038 0.009 −0.061 0.417** −0.093
0.999 0.009 0.001 0.169 0.833 0.376 0.779 0.949 0.648 0.001 0.486
18. AdCm _Mtng 1 −0.174 0.230 0.047 −0.135 −0.179 −0.083 −0.028 0.335* 0.348** 0.158
0.192 0.082 0.724 0.313 0.180 0.536 0.832 0.010 0.007 0.237
19. AF/TF 1 0.313* 0.045 0.033 −0.020 −0.130 0.026 −0.074 −0.234 −0.191
0.017 0.736 0.805 0.879 0.329 0.845 0.580 0.077 0.151
20. CSR_Com 1 −0.025 −0.129 −0.120 −0.095 −0.129 0.008 0.336** −0.150
0.850 0.334 0.370 0.476 0.334 0.954 0.010 0.261
21. DE 1 0.260* 0.505** 0.426** 0.016 0.000 0.239 −0.063
0.049 0.000 0.001 0.903 0.999 0.071 0.638
22. MTBVR 1 0.538** 0.417** 0.115 0.043 0.399** 0.100
0.000 0.001 0.392 0.750 0.002 0.453
23. ROA 1 0.634** 0.298* −0.065 0.437** 0.383**
0.000 0.023 0.627 0.001 0.003
24. CF 1 0.207 0.025 −0.251 0.301*
0.120 0.854 0.057 0.022
25. Capex/NCA 1 0.365** −0.082 0.006
0.005 0.543 0.964
26. Ln_AGE 1 0.081 0.279*
0.545 0.034
27. LogTA 1 −0.041
0.759
28. DivDum 1

Notes: The results of correlation between the variables used in this study are presented here. The correlation coefficients and their significance are disclosed.

* Correlation is significant at the 0.05 level (two-tailed).

** Correlation is significant at the 0.01 level (two-tailed)

Table A4

Rotated Component Matrix.

Aspect of CG Component
1 2 3 4 5 6 7
CGF#1 CGF#2 CGF#3 CGF#4 CGF#5 CGF#6 CGF#7
Panel A: Total variance
Variance retained (%) 14.529 11.445 11.310 11.095 7.310 7.086 7.067
Cumulative variance (%) 14.529 25.974 37.284 48.378 55.689 62.775 69.841
Panel B: Factor loadings
 1. AudCom_Size 0.856
 2. IDinAC 0.842
 3. CSR_Com 0.728
 4. No._of_BrdCom 0.718 0.446
 5. PSU 0.560 −0.406
 6. AFtoTF −0.401
 7. ROA 0.868
 8. DE −0.771
 9. CF_PBDITA/TA 0.730
10. MTBVR 0.610
11. PropID −0.793
12. NomCom −0.728
13. BS 0.662
14. BGA −0.465 0.453
15. DivDum 0.420
16. INSTH 0.891
17. PGH −0.873
18. FII 0.686
19. PropNED 0.768
20. CEODuality 0.651
21. Ln_TA 0.406 −0.452 −0.494
22. AudCom_Mtng 0.784
23. Capex/NCA 0.816
24. Ln_AGE −0.489

Notes: The results of the rotated component matrix are shown here. Extraction method: principal component analysis. The Rotation Method used is Varimax with Kaiser Normalisation. Rotation converged in 20 iterations.

Table A5

Results of Regression Analysis.

Model I II III IV
CCE/TA CCE/NA CA/TA CA/NA
CGF#1 −0.016 (0.900) −0.001 (0.992) −0.079 (0.447) −0.032 (0.758)
CGF#2 0.318 (0.015)* 0.254 (0.054)* −0.045 (0.664) 0.122 (0.251)
CGF#3 −0.081 (0.521) −0.107 (0.410) −0.147 (0.159) −0.174 (0.104)
CGF#4 0.055 (0.665) 0.101 (0.434) −0.065 (0.526) −0.004 (0.973)
CGF#5 0.003 (0.982) 0.017 (0.896) 0.070 (0.500) 0.053 (0.617)
CGF#6 0.086 (0.495) 0.088 (0.494) 0.210 (0.046)* −0.114 (0.282)
CGF#7 0.309 (0.017)* 0.288 (0.029)* 0.624 (0.000)** 0.623 (0.000)**
R2 21.4% 17.7% 47.3% 45%
Constant 9.921 (0.000) 6.397 (0.000) 22.518 (0.000) 17.545 (0.000)

Notes: The results in this table examine whether the dependent variables are influenced by the CG and financial factors derived using principal component analysis. The standardized coefficients (beta) and their significance, within parentheses, are disclosed.

* Coefficient is significant at the 5% level.

** Coefficient is significant at the 1% level.