Board Quality and Earnings Management: Evidence from India

Abstract

This article examines whether board qualities influence the earnings management behaviour of firms in a large emerging market set-up by using panel data of 783 Indian private manufacturing firms over a period of 7 years (April 2009–March 2016). The study finds that it is board quality that helps in curbing earnings manipulation and not just board independence. Results reveal that diligent and busy boards help in reducing earnings management, CEO duality affects the quality of reported earnings and promoters’ influence on boards increases earnings management. Domestic or foreign institutional investors do not have any independent impact on earnings management. However, domestic institutional ownership reduces earnings management when promoters’ influence exists. The article contributes to the literature by focusing on whether corporate governance (CG) mechanisms are important in curbing earnings management in an emerging market context. The findings are expected to be helpful to policymakers and regulators while framing appropriate CG policies and regulations.

Keywords

Corporate governance earnings management emerging economies board of directors discretionary accruals India

Introduction

Firms often resort to earnings management practices with a variety of motives. Several factors may motivate managers to manage firms’ earnings, but ‘market reaction’ has remained one of the most important factors (Bartov, Givoly, & Hayn, 2002; Erickson & Wang, 1999; Friedlan, 1994). If reported earnings of a firm are markedly less than the estimated earnings, it may adversely impact its share prices and if this trend continues, it may give rise to questions in the market about the earnings’ stability of the firm. Hence, managers are often motivated to use earnings management to smooth out fluctuations in earnings (Barnea, Ronen, & Sadan, 1976). Receiving increased amount of bonus, fear of government investigation and intervention may also motivate managers towards earnings manipulation (Haw, Qi, Wu, & Wu, 2005; Healy, 1985; Jones, 1991; Watts & Zimmerman, 1978).

Also, managers of a firm facing continuous earnings decline, may have their bonuses cut, loss of reputation and even the fear of being replaced (Gilson, 1989; Liberty & Zimmerman, 1986). These may also drive managers towards managing earnings of their firm. The existing ‘accrual accounting system’¹

As per ‘accrual accounting system’, all the expenses and revenues pertaining to a financial year are recorded in the books of accounts irrespective of the fact whether the expenses are actually paid or not and whether the revenues are actually received or not during that financial year. All Indian companies do follow ‘accrual accounting system’ for preparing their financial statements.

gives managers a wide range of discretion in determining their reported earnings. Managers have adequate control on the timing of actual expense items, and they can even alter the timing of recognition of revenues and expenses (Epps & Ismail, 2009; Teoh, Welch, & Wong, 1998). If managers’ incentives are dependent on firms’ financial performance, they may often try to manipulate earnings to fulfil their self-interests.

Why do managers manipulate earnings is an important question to the investors, policymakers and regulators. Whatsoever, earnings management affects investors by reporting false information which may distort investors’ decisions. Empirical studies show that investors give more importance to earnings than any other measures of firm performance like cash flows or dividends (Francis, Schipper, & Vincent, 2003; Liu, Nissim, & Thomas, 2002). Earnings management can have a significant impact on all stakeholders of a firm including the economy and society at large. This raises the question as to what can protect the interest of the stakeholders with regard to earnings management and here emerges the role of the corporate governance (CG) system in place. The agency theory recognizes the inherent divergence of interest between managers and owners of the firms (Jensen & Meckling, 1976). An effective CG system ensures that managers of a firm work at the best interests of shareholders and report the true financial position of the firm. Several studies in the UK and the USA show that company boards influence earnings management and the quality of financial statements (Sarkar & Sarkar, 2000). Cheng and Warfield (2005) provide the link between CG and earnings management and find that firms with higher managerial ownership have lower levels of earnings management. Existing studies also exhibit the relationship between board independence and earnings management (Beasley, 1996; Dechow, Sloan, & Sweeney, 1996; Klein, 2002; Peasnell, Pope, & Young, 2005; Xie, Davidson, & DaDalt, 2003).

Worldwide, many reforms have been initiated to strengthen CG practices to restore investors’ confidence on financial reporting practices of publicly traded firms (European Commission, 2003; Sarbanes-Oxley Act, 2002). The underlying belief behind all such initiatives is that an effective CG mechanism helps in enhancing the quality of reported earnings through proper monitoring of managers in the financial reporting process (Cohen, Krishnamoorthy, & Wright, 2004). Existing literature provides evidence that effective CG systems are capable of monitoring managements’ functioning and thereby enhance quality of reported earnings (Balsam, Krishnan, & Yang, 2003; Dey, 2008; Gul, Fung, & Jaggi, 2009; Lin & Hwang, 2010; Lobo & Zhou, 2006; Shen & Chih, 2007).

This article attempts to empirically examine the relationship of earnings management of firms with their board characteristics to understand whether board quality impacts earnings management. There are several motivations behind this study. First, even though several studies have examined the role of CG on earnings management, most of them are based on developed economies. Very little focus has so far been given on emerging economies, in spite of the fact that earnings management practices are more prevalent in emerging economies, compared to the developed economies (Agrawal & Chatterjee, 2015; Bhattacharya, Daouk, & Welker, 2003). There is acute dearth of studies examining the impact of board qualities on earnings management in the Indian context. As per the available literature, this is the first study to examine the role of the board’s quality on earnings management in the Indian context by using such a large number of sample firms, especially in the post-crisis (2008) period. Second, another interesting aspect is the ownership structure of Indian firms. Unlike a widespread equity model in developed economies, a majority of Indian firms are owned or otherwise controlled by founding families (known as promoters). Inside owners of firms belonging to emerging economies may be motivated to manage earnings to conceal true financial pictures of firms from the outside world for extracting private benefits (Haw, Hu, Hwang, & Wu, 2004; Leuz, Nanda, & Wysocki, 2003). In this relevance, policymakers, regulators, investors and other stakeholders have become sufficiently aware of the need for an effective CG system to ensure true and fair disclosure of corporate financial reports. This article is expected to contribute to the literature by establishing the impact of various board characteristics on earnings management, if any, in the context of a large emerging economy. The results are expected to be immensely beneficial to the investors, regulators, managers and researchers with a special focus on a large emerging economy.

The remainder of the article continues as follows. The second section presents review of literature. The third section highlights the objective of the study. The fourth section explains the data and methodology. The fifth section provides analysis of empirical results, and the final section concludes the study.

Literature Review

CG is primarily concerned with managing business with accountability, fairness, transparency and responsibility to protect the interest of all stakeholders. Setting effective governance policy helps in avoiding management-level corruption and creating shareholders’ value. Agency theory explains the conflicts of interests between managers and shareholders. Proper CG mechanisms mitigate the agency cost by monitoring managers’ activities and reducing opportunistic behaviour of managers (Ashbaugh, Collins, & LaFond, 2004). The composition of board of directors (board) is considered to be the most important aspect of CG. The real challenge before the board is to ensure a balance between protecting shareholders’ interests and simultaneously providing autonomy to the managers to function effectively. This section of the article concentrates on discussing the relevant literature.

Board Independence

The existence of independent directors (IDs) on the board is considered to be an extremely important aspect of board composition. IDs are selected for protecting shareholders’ interests (Rosenstein & Wyatt, 1990). As per CG norms in India, at least 50 per cent of the board members should be independent for listed firms with executive chairpersons, while for the listed firms with non-executive chairpersons, at least one-third of the board members should be independent. Since IDs do not have any direct or indirect benefits linked to the financial performance of firms, they are expected to facilitate reporting of true financial positions of firms and thereby curb possibility of earnings manipulation, if any. However, studies like Core, Holthausen, and Larcker (1999) and Kiel and Nicholson (2003) do not find empirical evidence that IDs create more effective boards and improve firm performance. On the other hand, Beasley (1996) and Peasnell et al. (2005) found that IDs play an effective role in curbing earnings management. Dechow et al. (1996) provide evidence that firms with majority independent boards prevent earnings management. Davidson, Goodwin, and Kent (2005), in their study of 434 listed Australian firms, argue that the majority of non-executive directors on the board and on the audit committee are found to be effective in reducing earnings manipulation. Although existing studies provide an inconclusive picture about the effectiveness of board independence in curbing earnings management, from a theoretical perspective, we can argue that IDs are expected to work in the best interest of the shareholders and thereby help in reducing earnings manipulation.

Board Busyness and Board Diligence

Existing literature highlights potential benefits of multiple directorships. Some studies find that having directors on board with multiple appointments results in better governance and constrains opportunistic earnings management (Fama, 1980; Fama & Jensen, 1983). Another set of studies argue that too many multiple appointments may cause difficulties for directors to discharge their professional responsibilities effectively (Ferris, Jagannathan, & Pritchard, 2003; Sarkar & Sarkar 2005). Finding adequate time to effectively discharge professional responsibilities is a real challenge to the directors (Lipton & Lorsch, 1992). Kang and Shivdasani (1995) and Khanna and Rivkin (2001) argue that in an emerging economy like India, due to domination of family-controlled businesses, busy boards may be considered an outcome of creating synergies between different directorial responsibilities. Some authors also argue that firms in emerging economies use directorial interlocks to ensure better synchronization with other firms and reduce ambiguity (Au, Peng, & Wang, 2000; Burt, 1983). According to The Companies Act (2013), a person can be the director of 20 Indian companies at the same time, provided that the maximum number of public companies in which a person can be appointed as a director shall not exceed 10. In a nutshell, the existing literature provides a mixed evidence about the relationship between multiple directorships and earnings management.

In this context, the present study includes another variable called board diligence as an additional important measure of board quality. Board diligence implies the mean percentage of the board meetings attended by directors. Studies like Carney and Gedajlovic (2002) and Gomes-Mejia, Larraza-Kintana, and Makri (2003) point out that boards of family-owned corporations, which are widely prevalent in India, are often dominated by promoters with substantial ownership rights and occupy key managerial positions to control firms. Such controlling shareholders have incentives to engage in opportunistic earnings management activities (Bertrand, Mehta, & Mullainathan, 2002; Fan & Wong, 2002; Friedman, Johnson, & Mitton, 2003). A diligent board is likely to monitor managers better and thereby curb their opportunistic behaviour. Broadly, studies in CG literature have not considered this important aspect seriously. However, Sarkar, Sarkar, and Sen (2008) document that a diligent board is effective in curbing earnings manipulation by firms.

CEO Duality

In CG literature, the effectiveness of the board is viewed as a function of its independence, size and leadership structure (Epps & Ismail, 2009). Prior studies on CEO duality have primarily focused on firms’ performance evaluation and comparison, and the results have been somewhat mixed. Braun and Sharma (2007) find no evidence behind the influence of CEO duality on firm performance in family-controlled publicly traded firms. However, they find that when family ownership is less, CEO duality is beneficial, measured by shareholders’ returns. Some studies also provide evidence that firms with separate chairpersons outperform firms with CEO dominance (Brickley, Coles, & Jarrell, 1997; Chagnati, Mahajan, & Sharma, 1985; Rechner & Dalton, 1991). Jensen (1993) reports that the dual office structure also allows the CEO to control information available to the board and consequently impede effective monitoring. However, Xie et al. (2003) find no relation between CEO dominance and earnings management. Dechow et al. (1996) argue that firms whose CEOs are also the chairpersons of boards are more likely to be subject to accounting enforcement action by Securities and Exchange Commission (SEC) for any violation of the accounting principles. However, empirical evidence on the relationship between CEO duality and earnings management is very less in emerging economies like India. Sarkar et al. (2008) exhibit that the presence of CEO-chairman affects the quality of the reported earnings of Indian firms.

Promoters’ Influence

In emerging economies like India, family-owned corporations dominate the industrial landscape, and the founding family members (i.e., promoters) often occupy key managerial positions with substantial ownership rights (Carney & Gedajlovic, 2002). Therefore, it is important to examine whether the presence of controlling shareholders on firms’ boards stimulates opportunistic earnings management. Existing literature highlights the incentives of controlling shareholders behind earnings management by camouflaging financial statements for their private benefits (Bertrand et al., 2002; Fan & Wong, 2002; Friedman et al., 2003). These studies are mostly based on developed economies. However, Sarkar et al. (2008), in their study on Indian firms, find that promoters’ presence on boards adversely impacts the quality of reported earnings.

Objective of the Study

The main objective of this study is to empirically examine how various board characteristics influence earnings management behaviour of firms in the Indian corporate sector. In other words, the study attempts to explore the linkage between board quality and earnings management, if any, in the context of a large emerging economy.

Data and Methodology

Data Source and Sample Frame

The sample for this study comprises 783 private manufacturing firms listed on the National Stock Exchange (NSE) of India Ltd.²

The National Stock Exchange (NSE) is the leading stock exchange in India and the fourth largest in the world by equity trading volume in 2015, according to World Federation of Exchanges (WFE). It has been the largest stock exchange in India in terms of total and average daily turnover for equity shares every year since 1995, based on annual reports of Securities and Exchange Board of India (SEBI).

The study period is from April 2009 to March 2016. The sample selection process involves a number of steps. First, in the initial sample, 1,029 manufacturing firms were available on NSE during the study period. Second, the study excludes government-owned companies, as their governance mechanisms are not entirely linked to the market. Third, the study removes companies with missing CG reports. Fourth, the study also excludes companies with missing accounting information required for necessary calculations for earnings management and finally, the study drops the companies for which discretionary accruals (DAs) (a proxy for earnings management, discussed in ‘Methodology’ section) could not be computed due to less than 10 firms in the industry. Banking and insurance sector companies are not the part of the sample, as they are subject to tight regulatory norms, and hence have limited scope for earnings management. Table 1 narrates the sample selection procedure.

Table 1.

Procedures of Sample Selection

Parameter	Number of Firms
Initial sample of manufacturing firms in the NSE	1,029
Exclude government-owned firms	(22)
Exclude firms with missing corporate governance reports	(97)
Exclude firms with missing accounting information	(92)
Exclude firms for which DA^* could not be calculated due to less
than 10 firms in the industry (over 2009–2016)	(35)
Final sample	783
Number of firm-year observations used in the study
(783 firms × 7 years)	5,481

Source: The author.

Note: *DA indicates discretionary accruals.

Data pertaining to various accounting variables and control variables are procured from ‘Prowess IQ’, a widely used and highly reliable corporate database, maintained by Centre for Monitoring Indian Economy (CMIE). Published annual reports and CG reports of the sample firms have been used for computing variables relating to board characteristics. The ‘Prowess IQ’ database has formed the basis of several published empirical studies on the Indian corporate sector (Bertrand et al., 2002; Chatterjee & Mukherjee, 2015; Khanna & Palepu, 2000).

The 783 sample manufacturing firms are spread across 19 sectors. Table 2 presents the industry-wise classification of the sample firms.

Table 2.

Industry-wise Classification of the Sample Firms

Industry	NIC Code*	No. of Firms
Other agricultural products	11	15
Tea	12	12
Food products	10	51
Textiles	13	57
Readymade garments	14	24
Paper and newsprints	17	26
Chemical and chemical products	20	92
Drugs and pharmaceuticals	21	76
Rubber and plastic products	22	40
Non-metallic mineral products	23	57
Basic metals (steel pipes and tubes, etc.)	24	65
Metal products	25	23
Electronics and others	26	26
Electrical equipment	27	51
Industrial machinery and others	28	47
Other transport	30	52
Gems and jewellery	32	26
Diversified	34	32
Books and media products	58	11
Total		783

Source: The author.

Note: *refers to the National Industrial Classification (NIC) code up to the first two-digit level.

Measurement of Earnings Management

As DAs are important tools for implementing earnings management, several past studies, such as Agrawal and Chatterjee (2015), Krishnan (2003), and Siregar and Utama (2008), have used DAs as a proxy for earnings management. This article also uses DAs as a proxy for earnings management. DAs represent those accruals which do not come out of normal operational conditions or the business environment but by the managers’ choice and directions in selecting various accounting methods and operational decisions. Researchers have used several models to measure DAs. Healy (1985), DeAngelo (1986), Dechow et al. (1996) and Jones (1991) are the most widely used models for measuring DAs. Guay, Kothari, and Watts (1996) argue that Jones model and modified Jones model are the only models which provide evidence that is consistent with opportunism as well as performance measure hypothesis. However, Dechow, Sloan, and Sweeney (1995) find that modified Jones model outperforms Jones model. The modified Jones model has been extensively used in the literature for estimating DAs (Agrawal & Chatterjee, 2015; Aref, Nejat, & Mohammad, 2012; Chen, Chen, & Huang, 2010; Jagdish, Khondkar, SangHyun, & Ziwen, 2014; Toto, Ari, & Yashinta, 2014).

Hence, this article uses the cross-sectional modified Jones model for estimating DAs. We estimate model coefficients from cross-sectional industry regressions by the first two-digit National Industrial Classification (NIC) code for each year. We require a minimum of 10 observations for each first two-digit NIC code and year combination. To be more specific, the coefficients in Equation (1) for firm i in industry I are estimated through cross-sectional regressions, with all firms having the same first two-digit NIC codes of industry I as firm i:

\frac{{TA}_{i t}}{A_{i t - 1}} = β_{1} (\frac{1}{A_{i t - 1}}) + β_{2} (\frac{Δ {REV}_{i t} - Δ {REC}_{i t}}{A_{i t - 1}}) + β_{3} (\frac{{PPE}_{i t}}{A_{i t - 1}}) + ε_{i t}

(1)

where

TA_it: Total accruals for firm i in industry I in period t, estimated as the difference between net income before extraordinary items and cash flow from operating activities

A_it-1: Firm i’s (in industry I) total assets in time period t − 1

ΔREV_it: Change in net sales revenue for firm i in industry I between time periods t and t − 1

ΔREC_it: Change in net receivables for firm i in industry I between time periods t and t − 1

PPE_it: Firm i’s (in industry I) total property, plant and equipment (i.e., fixed assets) in time period t

ε_it: The error term

Estimated values of the parameters β₁, β₂ and β₃ are then used in the following model to estimate the non-discretionary accruals (NDA_it) for each individual firm in industry I as follows:

{NDA}_{i t} = {\hat{β}}_{1} (\frac{1}{A_{i t - 1}}) + {\hat{β}}_{2} (\frac{Δ {REV}_{i t} - Δ {REC}_{i t}}{A_{i t - 1}}) + {\hat{β}}_{3} (\frac{{PPE}_{i t}}{A_{i t - 1}}) + ε_{i t}

(2)

where:

NDA_it: Firm i’s NDA in time period t

Finally, DAs are computed by subtracting NDAs from the total accruals as follows:

{DA}_{i t} = \frac{{TA}_{i t}}{A_{i t - 1}} - {NDA}_{i t}

(3)

where

DA_it: Firm i’s DA in time period t

To examine the effect of board characteristics on earnings management, this study has considered five board characteristics namely (a) board independence, (b) board busyness, (c) board diligence, (d) CEO duality and (e) promoters’ influence.

Control Variables

Apart from board characteristics, various firm-level factors may also impact earnings management behaviour of firms. Controlling these factors is considered to be important in empirical research. Existing studies find that size of a firm (size) is likely to affect earnings management (Agrawal & Chatterjee, 2015; Chen et al., 2010; Charitou, Lambertides, & Trigeorgis, 2011; Habib, Bhuiyan, & Islam, 2013; Selahudin, Zakaria, & Sanusi, 2014). Studies like Barton and Simko (2002) and Habib et al. (2013) argue that larger firms are more likely to engage in earnings management compared to smaller firms due to more pressure to beat analysists’ expectations and greater bargaining power with the creditors. However, Agrawal and Chatterjee (2015) find no association between firm size and earnings management. The degree of financial leverage (LEV) is also likely to affect earnings management of firms (Agrawal & Chatterjee, 2015; Charitou et al., 2011; Chen et al., 2010; Habib et al., 2013). It is observed that highly levered firms tend to engage in greater earnings management to avoid debt covenant violations (DeFond & Jiambalvo, 1994; DeFond & Park, 1997; Press & Weintrop, 1990). Earnings management is also likely to be influenced by cash flow from operations (CFC) of firms (Charitou et al., 2011; Chen et al., 2010; Habib et al., 2013). Existing studies find a negative relationship between cash flow from operations and earnings management, arguing that firms which do not have cash flow shortage are less prone to earnings management (Becker, DeFond, Jiambalvo, & Subramanyam, 1998; Kim, Chung, & Firth, 2003). Firms with adequate and uninterrupted cash flows are less likely to engage in earnings management (Agrawal & Chatterjee, 2015).

Firms’ profitability (measured by Return on Assets) and growth (measured by market-to-book (M/B) ratio) are also found to be associated with earnings management (Agrawal & Chatterjee, 2015; Kothari, Leone, & Wasley, 2005; Skinner & Solan, 2002). Nasuhiyah, Koh, Tan, and Wong (1994) observe that firms with low profits try to inflate earnings to report earnings above a certain level in order to avoid the risk arising out of earnings fluctuations. Chen et al. (2010) also find similar results. Robin and Wu (2012) argue that managers of high-growth firms tend to manage earnings to signal future performance of their firms. Sarkar et al. (2008) report that high-growth firms are associated with higher earnings management. The present study considers these five variables (namely, firm size, leverage, cash flow coverage (CFC), profitability and firm’s growth) as control variables in the regression models.

Equity holdings by institutional investors is another important aspect to be considered, especially with reference to emerging economies like India, where ownership is concentrated in the hands of promoters. In such a type of ownership framework, institutional investors are likely to play a significant role in firms’ governance and constrain earnings manipulation. Using a sample of US firms over 1988–1996, Chung, Firth, and Kim (2002) argue that large institutional shareholders constrain opportunistic earnings management. Mitra and Cready (2005) also report similar evidence. For examining the role of institutional investors in earnings management, if any, this study includes two more variables, namely equity shareholdings by domestic institutional investors (DII share) and equity shareholdings by foreign institutional investors (FII share). DII comprises Indian financial institutions, Indian banks and insurance companies and Indian mutual funds. All other institutional investors are categorized as FIIs.

All regressions include industry dummies to capture unobserved industry effects, if any. Measurements and definitions of all governance as well as control variables are presented in Table 3.

Table 3.

List of Variables and Definitions

Variable Names	Definitions
Percent_ID	Percentage of independent directors on the board. Computed as the number of independent directors divided by total board size, multiplied by 100.
Board_busyness	Board busyness. Dummy variable. Equals one if majority of directors hold three or more directorships, and zero otherwise.
Board_diligence	Board diligence. Calculated as mean percentage of board meetings attended by directors.
CEO_duality	CEO duality. Dummy variable. Equals one if the CEO is also the chairman of the board, and zero otherwise.
Promoter_influence	Promoter influence. Dummy variable. Equals one if controlling shareholders or promoters are present on the boards in the capacity of executive director, and zero otherwise.
DII_share	Domestic institutional investors’ share. Computed as percentage of equity owned by Indian financial institutions, Indian mutual funds, Indian banks and insurance companies.
FII_share	Foreign institutional investors’ share. Calculated as percentage of equity owned by institutional investors that are not domestic institutional investors.
Size	Measured by natural log value of total assets.
LEV	Degree of financial leverage derived as long-term debt divided by total assets.
MB ratio	Market-to-book value ratio, proxy for growth of the firm. Calculated as ratio of market value of equity over book value of assets.
CFC	Cash flow coverage. Derived as cash flow from operations plus cash and cash equivalents, divided by interest expense.
Return on Assets (ROA)	Measurement of firms’ profitability. Computed as net income divided by total assets, multiplied by 100.

Source: The author.

The Regression Model

This article considers 783 firms over a period of 7 years (April 2009–March 2016). This represents a panel data, and the main panel data regression used in this study takes the following form:

\begin{array}{l} {DA}_{i t} = \propto + β_{1} {(board independence)}_{i t} + β_{2} {(board busyness)}_{i t} + β_{3} {(board diligence)}_{i t} \\ + β_{4} {(CEO duality)}_{i t} + β_{5} {(promoters'influence)}_{i t} + β_{6} {(DII share)}_{i t} \\ + β_{7} {(FII share)}_{i t} + β_{8} {(Size)}_{i t} + β_{9} {(Lev)}_{i t} + β_{10} {(CFC)}_{i t} + β_{11} {(ROA)}_{i t} \\ + β_{12} {(MB Ratio)}_{i t} + Industry dummies + e_{i t} \end{array}

where

DA_it: Discretionary accruals of firm ‘i’ at time ‘t’

$\propto$ : The constant term

β₁, β₂, … β₁₂ : Regression coefficients

i: Number of firms (i = 1, 2, 3, …)

t: Number of years (t = 1, 2, 3, …)

e_it: The error term

Regression Model Assumptions: Linearity, Multicollinearity and Homoscedasticity

Before running the regression model, violations of the regression model assumptions, namely, linearity, multicollinearity and homoscedasticity, have been checked (Hair, Black, Babin, Anderson, & Tatham, 2008). Multicollinearity was checked using tolerance and variance inflation factor (VIF). In the specified model, lower VIF values (ranging from 1.072 to 1.533) and higher tolerance values (ranging from 0.652 to 0.933) explain the likelihood of low multicollinearity.

Further, Table 4 reports simple correlation matrix. It shows that the correlation coefficients are quite low among all the pairs, and in no case is it at a level that would indicate multicollinearity problem in our regressions.

For checking heteroscedasticity, which is a major problem in panel data analysis, the Breusch-Pagan-Godfrey test of heteroscedasticity is used as follows: (a) residuals are not heteroscedastic and (b) residuals are heteroscedastic.

From this test, the study found that the probability (p) values of chi-square of all the companies were not less than 5 per cent (unreported), which implies the acceptance of null hypothesis (H₀) that residuals of the model are not heteroscedastic. Also, linearity and homoscedasticity were examined by analysing residual plots (studentized residuals vs. standardized residuals). The results (unreported) report that the residuals of the dependent variable fell within a generally random pattern and depicted no pattern of decreasing or increasing residuals. Therefore, the linearity and homoscedasticity assumptions were confirmed.

Panel Regression Model: Model Fit Summary

In the panel data regressions, to decide whether fixed effect model or random effect model is appropriate, this article uses Hausman test (Hausman, 1978) to test the null hypothesis that the coefficients estimated by the efficient random effect estimator are the same as those estimated by the fixed effect estimator. The results of this test have rejected the null hypothesis (as the p value of F-statistics is found to be statistically significant at the 1 per cent level, p = 0.000), implying that the fixed effect model is appropriate_. Thus, Tables 5 and 6 report the fixed effect panel regression results.

Table 4.

Simple Correlation Matrix

Size (1)

1.00

CFC (2)

−0.07

1.00

LEV (3)

−0.01

0.03

1.00

ROA (4)

0.08**

0.25**

−0.09

1.00

M/B ratio (5)

−0.17

0.15**

−0.18

0.14**

1.00

ID % (6)

0.07

0.09*

0.04

0.25**

−0.07

1.00

Board busyness (7)

0.27**

0.10*

−0.09*

0.08*

0.10**

−0.06

1.00

Board_ Diligence (8)

−0.06**

−0.08

−0.01

−0.27

−0.08

−0.22

−0.05

1.00

CEO duality (9)

−0.07

−0.10

0.06

−0.02

−0.08

0.17

−0.11

−0.04

1.00

Promoter_ influence (10)

−0.10

0.03

0.02

−0.17

−0.01

0.03

−0.01**

0.15

0.09**

1.00

DII share (11)

0.28**

0.05

0.09*

0.05**

−0.08

0.00

0.16**

−0.14

−0.15

−0.21

1.00

FII share (12)

0.46

0.03

0.00

0.14**

−0.01

0.09**

0.18**

−0.09

−0.13

−0.26**

0.18**

1.00

Source: The author.

Note: Asterisks indicate the significance at the 0.05(**) and 0.10(*) levels.

In order to test the presence of autocorrelation, if any, in the residuals, the Durbin-Watson (DW) statistic is used. According to thumb rule, if the DW value is 2, one may assume the non-existence to first-order autocorrelations, either positive or negative. The values of DW statistic in Tables 5 and 6 signify the non-existence of severe autocorrelations in the residuals.

Endogeneity

The test of endogeneity, if any, is another important aspect. Studies like Himmelberg, Hubbard, and Palia (1999) and Yermack (1996) have used fixed effect panel specification to overcome the estimation issues related to endogeneity. If the unobservable characteristics are constant over time for a firm, then one can use fixed effect panel to generate consistent parameter estimates, robust to unobservable heterogeneity (Petersen, 2009). This assumption is reasonable for a panel data set with a small time series and large cross-section, as unobservable firm characteristics are unlikely to vary significantly over a small period of time (Schultz, Tan, & Walsh, 2010). Here, the panel data set (783 firms over 7 years) satisfies this criterion. Further, this article uses a formal test of endogeneity, the Darbin-Wu-Hausman (DWH) test (Durbin, 1954; Hausman, 1978; Wu, 1973) that tests the null hypothesis that regressors are exogenous. The p value of the DWH test statistic, which are not reported, is found to be more than 5 per cent (p value of 0.198), and hence this test could not reject the null hypothesis, signifying that endogeneity is not a significant concern here, and the fixed effect coefficient estimates are reliable.

Analysis

Summary Statistics

The summary statistics for the sample are presented in Table 5. Table 5 shows that the mean value of DA is 0.088, while the median value is 0.061. This indicates that some firms have relatively higher values of DA. Firm size has a mean value of 8.166, while the mean leverage is 0.43. The median values of these two variables are almost equal to their respective mean values, indicating that the sample provides a symmetric representation of firms of different sizes and leverages. The mean value of CFC is 3.27, while the median value is 1.25. This implies that some firms have relatively high values of CFC. A similar scenario is evident for firms’ profitability (measured through ROA) as well as firms’ growth (measured by M/B ratio).

With reference to board composition, Table 7 shows that the mean percentage of IDs is about 53 per cent. A total of 68 per cent of the firms have busy IDs on their boards, and directors have attended about 75 per cent of the board meetings. About 41 per cent of the firms have promoters as executive directors on their boards. Nearly 56 per cent of firms report CEO duality. Referring to equity ownership, we find that DIIs own on an average of only 2.60 per cent of equity of the sample firms, while average FII equity holding is about 3.60 per cent. For both DII and FII shares, the mean values are higher than the median values, implying that both types of institutions have large equity holdings in a few firms. DII share in some firm is as high as 25 per cent, and FII share is as high as 36 per cent (unreported).

Table 5.

Summary Statistics (Full Sample)

Variable	Mean	Median	Std. Deviation
Discretionary accruals (DA)	0.088	0.061	0.081
Percent_ID	52.542	50.002	11.449
Board_busyness	0.682	1	0.468
Board_diligence	75.497	78.351	17.689
Promoter_influence	0.413	0	0.372
CEO_duality	0.564	1	0.498
Size	8.166	7.690	1.371
Lev	0.429	0.413	0.175
MB_ratio	1.213	0.839	4.516
ROA	9.214	6.056	3.514
CFC	3.268	1.254	3.215
DII_share	2.591	0.415	4.159
FII_share	3.585	0.083	6.478

Source: The author.

Regression Results

The estimation of the regression of DAs on board composition is performed under two different specifications. Under the first specification, the effect of board composition on DA is examined by incorporating all control variables, excluding those which are related to institutional investors. Under the second specification, institutional investors, both domestic and foreign, are introduced in regression models. This will contribute in two ways. First, by doing so, one can examine whether the presence of institutional investors influences the relationship between board composition and earnings management. Second, it will help in examining whether institutional investors play any role in curbing earnings management.

Board Composition and Earnings Management

Table 6 presents the regression results for models 1 and 2. Here, two different regression models are estimated to examine the effect of board composition on DAs without institutional ownership. Model 1 examines the impact of board independence, board busyness and board diligence. Model 2 examines the influence of board independence, board busyness, board diligence and the influence of promoters and the CEO.

Table 6.

Effect of Board Composition on Discretionary Accruals

Explanatory Variables	Model 1	Model 2
Intercept	0.0800***(0.000)	0.0871***(0.000)
Percent_ID	−0.0010(0.406)	−0.0011(0.545)
Board_busyness	−0.0030**(0.042)	−0.0029**(0.043)
Board_diligence	−0.0009***(0.0039)	−0.0009***(0.0041)
Board_size	0.0012(0.461)	0.0016(0.394)
Promoter_influence		0.0034**(0.039)
CEO_duality		0.014**(0.019)
Lev	0.0049(0.311)	0.0047(0.433)
MB ratio	0.0070**(0.041)	0.0080**(0.048)
ROA	0.0061***(0.000)	0.0062***(0.000)
CFC	−0.0081***(0.000)	−0.0083***(0.000)
Industry dummies	YES	YES
No. of observations	5481	5481
R ²	0.3784	0.3807
F	32.119	32.315
D-W valueProb. F	1.8160.000	1.9020.000

Source: The author.

Notes: Values in parenthesis indicate p values.

*, ** and *** indicate that the coefficient is significant at 10, 5 and 1 per cent levels, respectively.

The results of Model 1 show that board independence has no impact on DAs (p value of 0.406). However, busy boards reduce earnings management as the coefficient is negative and is statistically significant at 5 per cent level. Also, diligence of directors reduces DAs, as the coefficient is negative and is statistically significant at 1 per cent level. These results remain the same in Model 2 as well. Further, Model 2 shows that promoters’ presence on the board enhances DAs, as the coefficient is statistically significant at 5 per cent level. CEO duality (where CEO of the firm is also the chairman of the board) is found to be positively associated with DAs, indicating that CEO duality leads to increase in earnings management. This finding is intuitive enough because dual chairman-CEOs often enjoy considerable power on the boards and hence may take decisions linked to self-interests rather than looking at the interest of outside investors.

In a nutshell, the regression results of Models 1 and 2 reflect that it is the diligence of the directors that reduces DAs and not the proportion of IDs on boards. Also, busy boards are found to be effective in reducing DAs. This signifies that multiple directorships held by directors of Indian firms are helpful in ensuring better governance, thereby improving the quality of reported earnings. While the presence of CEO duality results in increasing DAs, promoters’ presence on boards also increases earnings management.

Role of Institutional Investors

The article extends the regression analysis of board characteristics on DA after incorporating domestic and foreign institutional shareholdings, and results are presented in Table 7. Model 3 includes domestic institutional shareholding, model 4 includes foreign institutional shareholding and model 5 considers both domestic and foreign institutional shareholdings. Finally, model 6 shows the joint effect of both types of institutional investors and promoter’s influence on the board, along with all board characteristics and other control variables. Thus, model 6 is the full model examining the relationship between board composition and earnings management.

Table 7.

Effect of Board Composition and Institutional Investors on Discretionary Accruals

Explanatory Variables	Model 3	Model 4	Model 5	Model 6
Intercept	0.0870***(0.000)	0.0601***(0.000)	0.0690***(0.000)	0.0602***(0.000)
Percent_ID	0.0001(0.5460)	0.0002(0.6201)	0.0001(0.6105)	0.0002(0.6223)
Board_busyness	−0.0040**(0.0341)	−0.0032**(0.0293)	−0.0031**(0.0211)	−0.0050**(0.0317)
Board_diligence	−0.0009***(0.0032)	− 0.0008***(0.0029)	−0.0008***(0.0025)	− 0.0008***(0.0030)
Promoter_influence	0.0048**(0.0325)	0.0046**(0.0441)	0.0042**(0.0437)	0.0029**(0.0154)
CEO_duality	0.0140**(0.0251)	0.0170**(0.0279)	0.0161**(0.0216)	0.0270**(0.0426)
DII_share	0.0011(0.3411)		0.0012(0.3621)	0.0011(0.3052)
FII_share		−0.0019(0.2752)	−0.0018(0.2934)	−0.0019(0.2723)
Promoter_influence × DII_share				−0.0016**(0.0425)
Promoter_influence × FII_share				−0.0004(0.2961)
Size	−0.0031(0.1954)	−0.0052**(0.0381)	0.0051**(0.0324)	−0.0061**(0.0432)
Lev	−0.0035(0.4211)	−0.0039(0.3862)	−0.0038(0.3590)	−0.0057(0.2710)
MB ratio	0.0081**(0.0300)	0.0070**(0.0362)	0.0072**(0.0292)	0.0080**(0.0375)
ROA	0.0061***(0.0000)	0.0057***(0.0000)	0.0051**(0.0000)	0.0069***(0.0000)
CFC	−0.0080***(0.0000)	−0.0080***(0.0000)	0.0071***(0.0000)	−0.0081***(0.0000)
Industry dummies	YES	YES	YES	YES
No. of observations	5481	5481	5481	5481
R ²	0.3812	0.3901	0.3956	0.4025
F	32.357	33.112	33.58	34.165
D-W valueProb. F	1.9040.000	1.9150.000	1.8670.000	1.8940.000

Source: The author.

Note: Values in parenthesis indicate p values; *, ** and *** indicate that the coefficient is significant at 10, 5 and 1 per cent levels, respectively.

Results of Models 3 and 4 exhibit that various board characteristics continue to show similar results as shown in Models 1 and 2. Board independence does not affect DAs, while busy and diligent boards reduce earnings management. While CEO duality leads to increase in earnings manipulation, promoters’ presence on boards as executive directors also increases DAs. The coefficients of domestic institutional ownership in Model 3 and foreign institutional ownership in Model 4 are not found to be statistically significant (p values of 0.34 and –0.0019, respectively), signifying that neither domestic nor FII influences DAs and they are not associated with reduced earnings management. The results remain unchanged in Model 5, which considers both types of institutional shareholdings. This finding is contrary to the findings of Chen et al. (2010) and Mitra and Cready (2005) who exhibit that large institutional shareholders constrain opportunistic earnings management.

Thus, institutional shareholders, by themselves, do not have any significant impact on DAs. However, it is likely that powerful institutional investors can act as a catalyst in mitigating the influence of controlling promoters on board’s effectiveness. Large and powerful institutional shareholders are effective in mitigating the agency costs between controlling shareholders and outside shareholders, especially in the context of family-owned corporations (Anderson & Reeb, 2003; Sarkar et al., 2008). Applying this reasoning in an emerging market context (here, India), where family-owned corporations dominate the corporate landscape, one can argue that the presence of institutional investors can constrain opportunistic earnings management of the controlling insiders.

To address this issue, following Sarkar et al. (2008), this article analyses the joint effect of institutional shareholding and promoters’ presence on DAs. For estimating the joint effect, two interaction terms have been incorporated: interaction of domestic institutional ownership and promoters’ influence, as well as interaction of foreign institutional ownership and promoters’ influence. Model 6 reports the panel regression results. Model 6 shows that the coefficient of the interaction term linked to DII is negative and is statistically significant at 5 per cent level (p value of 0.043), signifying that DIIs reduce DAs where promoters’ influence exists. In this context, it is pertinent to mention that while promoters’ influence on boards increases DAs (as evident from the results of Models 3–5), domestic institutional ownership, in the presence of promoters’ influence, reduces DAs. However, the coefficient of the interaction term associated with foreign institutional ownership is found to be statistically insignificant (p value of 0.296), suggesting that FIIs do not play any compensatory role even in the presence of promoters’ influence. This difference in findings can be explained by the fact that DIIs especially banks, financial institutions and so on can keep their nominees on companies’ boards to participate and monitor their functioning and, therefore, can keep a close watch on the functioning and governance of those companies. However, for an outside institutional investor, it may be relatively difficult to assess the effectiveness of governance mechanisms in place. Very few studies in CG literature have examined the joint effect of these two alternative mechanisms (Cremers & Nair, 2003; Sarkar et al., 2008). This finding, that there is a mitigating effect on earnings management in the presence of both (DII share and promoters’ influence), has important implications for regulators while framing their policies as well for investors while taking investment decisions.

Impact of Other Control Variables

The results of Model 6 reveal that firm size is negatively associated with DA since the coefficient is negative and is statistically significant at 5 per cent level. This indicates that larger firms are less prone to earnings manipulation compared to smaller firms. The reason may be that larger firms have better internal control mechanisms, high-quality auditing practices and also high concern for the lost reputation in the event of earnings management detections, and this is why they are less engaged in earnings manipulation. M/B, a proxy for firm growth, is positively associated with DAs (p value of 0.037), indicating that high-growth firms are associated with higher earnings management. Possibly, high-growth firms manage their earnings to signal better future performance to attract investment. This finding is similar to the findings of Robin and Wu (2012) and Sarkar et al. (2008). The coefficient of CFC is found to be negative and is statistically significant at 1 per cent level (p value of 0.000). This simply indicates that firms with higher CFC have lesser incentives to manipulate their earnings. Becker et al. (1998) and Kim et al. (2003) also find similar results. Firms’ profitability is found to be positively related to DAs (p value of 0.000), indicating that profitable firms tend to manage their earnings. The reason may be managers of less profitable firms wish to achieve their target earnings level to fulfil their self-interest and/or ensure that investors do not lose confidence in their firms. On the other hand, firms with severe losses and distress level usually do not inflate their earnings; rather, they prefer to disclose their true financial difficulties in order to obtain better terms from their creditors and lenders. Chen et al. (2010), Jaggi, and Lee (2002) and Nasuhiyah et al. (1994) also argue in the same line. The coefficient of leverage is statistically insignificant (p value of 0.27), implying that financial leverage does not have any impact on earnings management. Finally, we find the existence of industry effect on DAs.

Conclusion and Implications

This article examines whether board qualities influence earnings management behaviour of firms in the context of a large emerging economy, India. By considering a wide range of board characteristics, namely, independence, busyness, diligence, promoters’ influence and CEO duality, this study contributes to the literature by empirically examining whether the governance mechanisms are effective in curbing earnings management. Using panel data analysis of 783 private manufacturing firms over 7 years, the study finds that it is the board quality that actually helps in curbing earnings manipulation and not just board independence. The results indicate that board independence does not necessarily help in curbing earnings management; rather, diligent boards are useful in reducing earnings management. Multiple directorships held by directors are also found to be effective in curbing earnings management. Another interesting finding of the study is that earnings management practice is more in those firms where CEO is also the chairman of the board of directors. This finding is important in an economy like India, where a majority of the firms report CEO duality (56% of the sample firms in this study). In the same line, results show that promoters’ influence on board increases earnings management. The article also reflects that in the existence of various board characteristics, domestic or FIIs do not have any independent effect on earnings management. However, domestic institutional ownership reduces earnings management when promoters’ influence exists. The study establishes the fact that a carefully designed board with certain qualities is helpful in curbing earnings management in an emerging market context.

The findings are expected to be helpful to policymakers and regulators while framing appropriate CG policies and regulations. For instance, when directors hold multiple directorships in several companies, it does not necessarily prevent them from undertaking their professional responsibilities effectively on boards. Also just increasing the proportion of IDs on boards does not ensure better governance and clear earnings reporting; rather, formation of diligent boards is a more important aspect to be considered. When the board of directors (both independent as well as non-IDs) attend regular board meetings and fulfil their professional responsibilities, then better quality of earnings reporting may be expected. However, whether the board of directors actually discharge their responsibilities effectively or not is an important and interesting research question to be examined and is outside the purview of this article. Finally, from the findings of this study, investors can gain meaningful insights about the quality of reported earnings by looking at the board compositions of the companies operating in a large emerging economy.

Footnotes

Acknowledgement

The author is grateful to International Management Institute Kolkata for providing a research grant to conduct this research. The author is also indebted to Ms Sanghita Ghosh for research assistance. The author is grateful to the anonymous referees of the journal for their extremely useful suggestions to improve the quality of the article. Usual disclaimers apply.

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 Research Grant from International Management Institute Kolkata, India.

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