This study aims to investigate how gender diversity and remuneration of boards of directors’ influence earnings quality for Spanish-listed firms.
The sample includes 105 nonfinancial Spanish firms from 2013 to 2018, corresponding to an unbalanced panel of 491 firm-year observations. The primary empirical method uses a Tobit semiparametric estimator with firm- and industry-level fixed effects and an innovative set of measures for earnings quality developed by StarMine.
Results exhibit a positive correlation between increased gender diversity and a firm’s earnings quality, suggesting that a gender-balanced board of directors is associated with more transparent financial reporting and informative earnings. We also find a nonmonotonic, concave relationship between board remuneration and earnings quality. This indicates that beyond a certain point, excessive board compensation leads to more opportunistic manipulation of financial reporting with subsequent degradation of earnings quality.
This study only covers nonfinancial Spanish listed firms and is silent about how alternative board features’ influence earnings quality and their informativeness.
This study introduces measures of earnings quality developed by StarMine that have not been used in the empirical literature before as well as measures of board gender diversity applied to a suitable Tobit semiparametric estimator for fixed effects that improves the precision of results. In addition, while most of the literature focuses on Anglo-Saxon countries, this study discusses board gender diversity and board remuneration in the underexplored context of Spain. Moreover, the hand-collected data set comprising financial reports provides previously untested board features as well as a nonlinear relationship between remuneration and earnings quality that has not been thoroughly discussed before.
1. Introduction
Earnings quality refers to the extent to which a firm’s reported earnings capture its actual earnings (Krishnan and Parsons, 2008). When the informational quality of reported financial statements is degraded through aggressive accounting practices, earnings quality is damaged (Martin et al., 2016) and agency problems arise (Jiraporn et al., 2008). These agency problems can be mitigated through effective corporate governance systems. From a corporate governance perspective, our study aims to test the effectiveness of two boards of directors’ features promoting earnings quality: the gender diversity and remuneration of the board of directors.
The OECD/G20 Principles on Corporate Governance establish that a governance framework should ensure the strategic guidance of a company, the effective monitoring of management and a board’s fiduciary accountability to the company and its shareholders [1]. In this spirit, Spain has developed strong corporate governance regulation. The Unified Good Governance Code of Listed Companies promotes modern corporate governance policies, promulgated by entities such as the OECD, the Basel Committee on Banking Supervision and the European Commission. Jensen (1993) identifies the role of boards of directors as an internal governance mechanism that mitigates agency conflicts. But improvements to the legal framework in Spain have not prevented financial scandals, highlighting the inability of boards of directors to inhibit aggressive accounting practices (Segui-Mas et al., 2018).
Managers can dilute earnings quality by manipulating financial reporting, leading to private benefits, and misleading outside investors (Aboody et al., 2005). Boards of directors have a crucial role in constraining these practices, but they do not always provide the transparency necessary for effective decision-making (Segui-Mas et al., 2018). Two central points that have been highlighted in social and political debates in the past few years, calling for the attention of regulators, scholars, corporations and users of financial information are the demand for gender equality in the labor market and other social spheres (Arun et al., 2015) and the questioned effectiveness of compensation packages paid to executives and board members (Merino-Madrid et al., 2009). Therefore, this paper sheds light on the social and political debate by examining the association of board gender diversity and the board remuneration system on firms’ earnings quality.
We make contributions on several fronts. First, we study earnings quality through a new, more informative metric based on the StarMine algorithm, which incorporates firms’ accruals, cash flow and operating efficiency in the measure of earnings quality. This is a forward-looking metric that combines information content relevant for future decisions; which differs from the traditional, accruals-based earnings-management measure widely used in the literature which acts as a backward-looking proxy for financial transparency (Lo, 2008).
Second, most empirical studies are based on Anglo-Saxon, common-law contexts and cannot be generalized to differing legal frameworks in civil-law countries. Indeed, La Porta et al. (2000) found evidence that common-law countries have the strongest protection of outside investors, including shareholders and creditors, in comparison to civil-law countries. Spain’s civil-law system is characterized by weaker protection of shareholder’s rights, heavier government intervention and higher self-dealing problems than common-law countries (La Porta et al., 2008). Because aggressive accounting and the subsequent deterioration of earnings quality are more likely to occur in countries with less protection for minority shareholders (Bushman et al., 2004), our study articulates a pertinent discussion of board gender diversity and board remuneration in a civil-law context. This study also builds upon the scarce literature on Spain, such as García-Sánchez et al. (2017), by analyzing new features such as earnings sustainability instead of earnings management.
Third, we calculated board gender diversity using the percentage of women on boards as well as the Blau and Shanon’s indices to capture gender equality, in line with the United Nations Sustainable Development Goal 5. Hence, this research is highly pertinent for the Spanish corporate sector and the European region, as contemporary debates about implementing directives seeking gender-balanced boards of directors and fair-remuneration policies for board members have become increasingly prominent in the corporate and social arenas (Damak, 2018).
Fourth, our analysis leads us to establish a nonlinear relationship between remuneration and earnings quality that has not been thoroughly discussed, nor empirically tested, in previous literature (Laux and Laux, 2009). Finally, we applied a new robust econometric technique based on the Tobit semiparametric estimator for fixed effects that improves the precision of results. We do this by using hand-collected data from the Spanish Stock Exchange Commission that allows us to extend the literature by finding relationships between previously untested board features.
We find that gender-balanced boards improve earnings quality of listed firms. Because most Spanish firms have low female representation on their boards, companies can benefit from promoting women to their board of directors to achieve gender-balanced board composition. Exhibited in Figure A1, boards are clearly male biased, with female participation being about half the average of the countries in the European Economic Area.
Female representation on boards of directors, and Rrmuneration per director over time
Female representation on boards of directors, and Rrmuneration per director over time
We also provide empirical evidence for Spain on the unexplored nonlinear relationship between board remuneration and firms’ earnings quality. This relationship has been theoretically suggested but not tested empirically. Earnings quality does not appear to monotonically increase with board compensation; rather, it is optimized when remuneration per board member is roughly €460,000, or 3.90% of a firm’s annual net income. While some Spanish listed firms are overpaying their board members, many could improve earnings quality by increasing board compensation.
Our findings are robust across three alternative measures of gender diversity and four metrics for board remuneration. We shed light on how board gender diversity and board remuneration policies can be used as drivers to improve firm financial transparency, as a response to stakeholder pressures like the United Nations with the Sustainable Development Goals call for action to all countries. Improving financial information quality is key to creating a healthy financial system in which corporations play a fundamental role (Pucheta-Martínez et al., 2016). Transparent and high-quality financial information is linked to good corporate governance practices (Segui-Mas et al., 2018).
2. Literature review
2.1 Board gender diversity and earnings quality
The agency perspective emphasizes that the major role of the board of directors is to monitor managerial decisions to protect the interest of shareholders and ensure the integrity and transparency of corporate financial information (Jensen and Meckling, 1976). Relevant characteristics of boards include their gender balance and their compensation schemes, which serve as monitoring mechanisms to enhance governance and the quality of financial reporting (Baixauli-Soler et al., 2016).
The agency approach suggests that gender balance on a board reduces agency costs (Orazalin, 2020) because it increases monitoring of managers and in turn aligns the interests of shareholders and managers (Campbell and Mínguez-Vera, 2008). Along the same lines, Dalton and Dalton (2010) suggest that diversity of board members improves the effectiveness of the board of directors and enables it to provide better oversight of a firm’s disclosures and reports. As further support, Adams and Ferreira (2009) and Gull et al. (2018) indicate that female directors are more cautious in their decision-making, demand greater accountability from managers for weak corporate performance and are more willing to report fraudulent financial disclosures. When men participate in decision-making, they aim mostly at improving the performance of the company. This could be because men are more confident in what they do in financial matters (Barber and Odean, 2001; Krishnan and Parsons, 2008). When women join the decision-making group, their vision, attitudes and character complement those of men, enhancing decisions, corporate performance and earnings quality (Kogut et al., 2014).
Men and women take different approaches to money and investment (Barber and Odean, 2001). Female executives are more conservative in their financial decisions than male executives, which contributes to less aggressive accounting practices (García-Sánchez et al., 2017). Similarly, gender inequality in leading corporate positions results in poor corporate governance (García-Izquierdo et al., 2018). According to Saeed et al. (2019), a gender-balanced board of directors benefits from the skills, experiences and backgrounds of its varied members and engages in a high-quality decision-making process that promotes effective monitoring of earnings management and subsequently improves earnings quality. Companies that balance their board of directors by increasing the number of female members constrain earnings-manipulation practices and enhance the quality of financial reporting (Pucheta-Martínez et al., 2016). Such gender diversity contributes to the quality of the board’s deliberations and discussions of difficult issues that are often unaddressed by all-male boards (Gul et al., 2011). Similarly, as Krishnan and Parsons (2008) emphasize, high gender diversity in higher levels of management is positively related to earnings quality.
Equality among men and women is a universal legal principle that was approved by the UN General Assembly in 1979 and endorsed by Spain in 1983. Gender parity is a fundamental principle in the European Union which is also included in the United Nations’ Sustainable Development Goal agenda [2], and Spain has incorporated voluntary and compulsory regulations regarding board gender diversity [3]. The Unified Good Governance Code of Listed Companies set Spain on a new path toward achieving gender-balanced boardrooms. The code initiated a long process of reform and more recently was amended to propose a minimum of 40% female representation [4]. In addition, Spain approved the Organic Law 03/2007 which gives women and men equal access to employment and equal working conditions, in addition to promoting parity of women and men on the boards of directors of large corporations by mandating that no gender can represent less than 40% nor more than 60% of the seats. Moreover, the Spanish Constitution proclaims the right to equality and nondiscrimination on the basis of gender. In addition, Spain became the third European country to establish not binding gender quota, where the absence of sanctions has hindered the achievement of the law’s objectives. [5] Therefore, based on the theoretical, empirical and contextual arguments on the Spanish corporate sector, we hypothesize H1:
There is a positive relationship between board gender diversity and a company’s earnings quality.
2.2 Board remuneration and earnings quality
Board compensation plans are designed to encourage directors to wisely advise managers, monitor their performance and ultimately maximize shareholders’ wealth (Jensen and Meckling, 1976). Merino-Madrid et al. (2009) state that there is no scholarly consensus about the optimal board-remuneration structure and that companies should offer compensation packages with an optimal combination of fixed and variable components to align interests and reduce agency costs. Designing such packages has been a priority on the governance-reform agenda (López et al., 2015) because high compensation compromises directors’ objectivity in their governance role (Brick et al., 2006). Prior research provides significant insight on how executive compensation affects earnings quality and corporate decisions (Amewu and Alagidede, 2019), but the relationship between board compensation and earnings quality is underexplored.
Spanish companies are subject to two regulations regarding board remuneration. The first is the Unified Good Governance Code of Listed Companies of 2006. The code recommends disclosing board members’ remuneration and making this information transparent. It is characterized by the voluntary “comply and explain” principle. The second regulation is the Spanish Companies Act (or Royal Legislative Decree 1/2010), which requires companies to disclose directors’ pay, among other obligations. Remuneration policies must specify the maximum amount that companies can distribute to directors. For Spanish listed firms, Merino-Madrid et al. (2009) highlight the lack of independence on board-remuneration committees for optimizing value creation, suggesting that an optimal compensation scheme should exist.
Two competing hypotheses attempt to explain the board compensation-earnings quality relationship. The incentive hypothesis suggests that higher pay provides directors with an incentive to monitor managers, which prevents earnings manipulation and subsequently improves earnings quality and earnings informativeness (Adams and Ferreira, 2008). High compensation attracts talented directors, and such compensation aligns the interests of directors and the owners they represent (Vafeas, 2000). Empirically, this hypothesis is confirmed by Kim et al. (2013), who suggest that cash-based compensation establishes a better agency relationship between directors and shareholders than equity-based compensation.
An alternative explanation is the perverse-incentive hypothesis, which posits that excessive cash compensation may encourage directors to pay managers back by reducing their oversight of the managers, triggering higher earnings manipulation and in turn reducing earnings quality. This quid pro quo is associated with excessive compensation and weak monitoring (Brick et al., 2006). Overcompensated directors may be less likely to criticize managers and more likely to give managers the discretion to misreport earnings for personal benefit. Excessive compensation may create an environment in which managers and directors can connive to enjoy private benefits at the expense of shareholders. It reduces the pressure for directors to engage in internal monitoring, which lets managers misrepresent earnings and causes earnings quality to deteriorate. Empirically, Ye (2014) documents a negative association between earnings quality and cash compensation for independent directors. This suggests that rewarding independent directors with high cash pay compromises their independence and reduces their effectiveness at overseeing managers’ financial reporting. Similarly, by closely tying directors’ wealth to equity prices, schemes that compensate directors with an excessive amount of stock may incentivize managers to misreport financial information. As emphasized by Ye, if the market cannot see through overstated earnings and if it misvalues a company, directors compensated with stock may benefit from altering financial reports by engaging in insider trading.
To our knowledge, these hypotheses have yet to be examined empirically. For Spanish firms, the nearest study is García-Izquierdo et al. (2018), which analyzes how CEO pay correlates to the ratio of females on a board. It does not analyze earnings quality, board remuneration nor gender-balanced boards. Ours does. Because the incentive hypothesis is expected to be confirmed at relatively low compensation levels (implying a positive impact of board compensation on earnings quality), while the perverse-incentive hypothesis is expected to be confirmed at excessive compensation levels (implying a negative impact of board compensation on earnings quality), we hypothesize that:
A concave relationship is expected between board remuneration and earnings quality.
3. Methodology
3.1 Data and econometric techniques
Our sample comprises 105 nonfinancial Spanish listed firms from 2013 to 2018 in an unbalanced panel with 491 firm-year observations, with an average of 4.7 continuous observations per company. Financial firms were excluded because of their unique industry features [6]. Information on financial statements was obtained from Thomson Reuters Refinitiv Eikon. Data on board features were manually collected from the Annual Report of Corporate Governance published by the Spanish Stock Exchange Commission.
Our methodology uses a Tobit semiparametric estimator with firm- and industry-fixed effects because of the censored nature of our alternative dependent variables as described below. To our knowledge, this is the first paper applying this econometric technique to include these fixed effects to study earnings quality [7]. In addition, to address the common endogeneity problems in corporate governance studies (Barros et al., 2019) as well as the individual, time-invariant heterogeneity problem, we apply the Generalized Method of Moments system estimator (GMM-SE) following Blundell and Bond (1998). The GMM in first differencing (Arellano and Bond, 1991) is used as a second-order method to deal with endogeneity issues. Further details on the Tobit and GMM methods are discussed in Appendix 1.
3.2 Variable definitions and model specification
3.2.1 Measures of earnings quality.
The primary dependent variables are measures of earnings quality developed by StarMine [8] and range from 0 to 1, with 1 being the highest. StarMine defines earnings quality as the degree to which past earnings are reliable and persistent. High-quality earnings accurately reflect a company’s current and past operating performance, indicate future operating performance and represent a reliable valuation measure for the company for all earnings levels. StarMine states that poor earnings quality indicates a likelihood of deteriorating fundamentals and low financial transparency, which makes it more difficult for investors to make informed investment decisions.
StarMine’s earnings-quality formula includes accruals, cash flow and operating efficiency. Our first measure of earnings quality is an overall measure that includes all three components (EQ1); our alternative metrics correspond to accruals (EQ2), cash flow (EQ3) and operating-efficiency (EQ4) measures of earnings quality. To our knowledge, this is the first study to use these metrics. Further details about the construction of these metrics are supplied in Appendix 2.
To mitigate bias in estimating the earnings-quality metric, we test the consistency of our findings with a popular measure based on discretionary accruals. Following the modified Jones model (Dechow et al., 1995), the discretionary components of total accruals (TAcc) correspond to the residuals of the model to account for annual changes in operating revenues (ΔREV), annual changes in accounts receivable (ΔAR) and property, plant and equipment (PPE) as follows:
This complementary measure of earnings quality is represented by an earnings-management metric (EM) estimated as the residuals of equation (1). Given that managers can engage in income-increasing or income-decreasing manipulation of financial information, we follow the literature in considering the absolute value of EM to improve precision (Dechow et al., 1995). To standardize the earning-quality variable, we multiply EM by −1. Hence, higher (lower) values of EM indicate better (worse) earnings quality, consistently with the StarMine-based metrics.
3.2.2 Measurement of independent variables.
The first explanatory variable is board gender diversity, defined as the percentage of women on the board (GD1). Given that a board composed only of male or female members exhibits a lack of diversity, we follow Saona et al. (2019) in using two additional indexed metrics to measure the construct of a gender-balanced board. The Blau (1977) diversification Index (GD2) is computed as , where Pi corresponds to the proportion of directors in each of the n = 2 gender categories (male and female). GD2 ranges between 0 (for no gender diversity) and 0.5 (equal proportions of male and female board members). The last metric is the Shannon (1948) diversification index (GD3), computed as . GD3 takes values between 0 (no gender diversification) and 0.693 (equal proportions of each gender) [9].
Directors’ compensation schemes are measured with the log transformation of the average total remuneration per director (BRem1) [10], the fixed remuneration per director (BRem2) [11] the board’s total remuneration (BRem3) and the board’s variable remuneration (BRem4), all as shares of the company’s net income. We compute the squared transformation of these variables to test their nonlinear relationship (posited in H2) with the earnings-quality measures.
We include several control variables. Following Gull et al. (2018), board-feature controls include the percentage of equity capital owned by the board (BOwn), the percentage of independent board members (BIndep), the number of board members (BSize) and the percentage of executive directors (BExec). Company characteristics include company size (Size) measured as the logarithm of total assets, a measure of default risk (Risk) based on Altman’s (1968) Z-score, the return on assets (ROA) computed as net income over total assets, the leverage ratio defined as total debt over total assets (Lev), a metric of growth opportunities (GO) defined as total assets less total common equity plus the company’s market capitalization. All controls are scaled by total assets [12].
Moreover, we introduce some dummy variables summarizing additional information on the degree to which Spanish companies follow Unified Good Governance Code. These variables refer to:
if there are requirements for chairmanship;
whether the board secretary monitors good-governance recommendations;
the existence of supermajorities;
whether tenure of 12 years or more of independent directors is required;
the existence of a secretary board member;
the existence of external advice for directors; and
the existence of policies concerning time to prepare for board meetings.
Because these dummy variables are highly correlated, we applied the Cluster-Focused Principal Component Factoring technique, which produced three factors (named Governance1, Governance2 and Governance3 that capture an accumulated 62.06% of the variability of the corresponding seven covariates.
This results in the following partial-identification specification:
Here EQit represents earnings quality based on the StarMine metrics and the discretionary-accruals measure for robustness purposes for company i in period t; δ1it represents one of the alternative measures of board gender diversity and δ2it represents one of our measures of board remuneration; the vector includes the set of controls previously defined; IndDummy represents the industry dummies capturing systematic differences across industry sectors; µi represents the individual effect; and εit is the mean-zero error term.
4. Results
4.1 Descriptive statistics
The firms in the sample are representative of the Spanish market [13].
As shown in Table A2 of Appendix 3, the earnings-quality measures indicate that the capacity of earnings to represent Spanish companies’ actual economic performance is average. The overall measure of earnings quality (EQ1) is less than 0.5 while the other three metrics are slightly greater than 0.5. As for board gender diversity, we observe that about 16% of board seats are occupied by women, well below the recommendations of the Organic Law 03/2007 and the 40% quota imposed in other European nations. The mean values of the alternative measures of gender diversity (GD2 and GD3) are also well below gender parity.
Descriptive statistics
| Variable | Description | Mean | SD | Min | Max | |
|---|---|---|---|---|---|---|
| Panel A: Earnings-quality measures | ||||||
| EQ1 | Country rank of earnings quality, defined by StarMine | Overall | 0.452 | 0.288 | 0.010 | 1.000 |
| Between | 0.224 | 0.010 | 0.888 | |||
| Within | 0.199 | −0.188 | 0.967 | |||
| EQ2 | Accrual measure of earnings quality, defined by StarMine | Overall | 0.503 | 0.263 | 0.010 | 1.000 |
| Between | 0.172 | 0.040 | 0.940 | |||
| Within | 0.218 | −0.077 | 1.106 | |||
| EQ3 | Cash flow measure of earnings quality, defined by StarMine | Overall | 0.558 | 0.222 | 0.040 | 1.000 |
| Between | 0.197 | 0.090 | 1.000 | |||
| Within | 0.127 | 0.045 | 1.060 | |||
| EQ4 | Operating-efficiency measure of earnings quality, defined by StarMine | Overall | 0.548 | 0.263 | 0.010 | 1.000 |
| Between | 0.230 | 0.055 | 0.978 | |||
| Within | 0.152 | −0.030 | 1.125 | |||
| EM | Earnings-management measure based on modified Jones’s model | Overall | −0.061*** | 0.108 | −0.941 | 0.000 |
| Between | 0.115 | −0.941 | 0.000 | |||
| Within | 0.075 | −0.760 | 0.324 | |||
| Panel B: Board-gender-diversity measures | ||||||
| GD1 | Percentage of female directors | Overall | 0.159 | 0.122 | 0.000 | 0.571 |
| Between | 0.105 | 0.000 | 0.514 | |||
| Within | 0.061 | −0.008 | 0.436 | |||
| GD2 | Blau’s (1977) index of gender diversification | Overall | 0.238 | 0.152 | 0.000 | 0.500 |
| Between | 0.134 | 0.000 | 0.487 | |||
| Within | 0.077 | 0.019 | 0.538 | |||
| GD3 | Shannon’s (1948) index of gender diversification | Overall | 0.374 | 0.218 | 0.000 | 0.693 |
| Between | 0.193 | 0.000 | 0.680 | |||
| Within | 0.113 | 0.023 | 0.823 | |||
| Panel C: Board-remuneration measures | ||||||
| BRem1 | Log transform of total remuneration per director (€) | Overall | 12.119 | 1.184 | 7.041 | 14.630 |
| Between | 1.227 | 7.118 | 14.057 | |||
| Within | 0.358 | 10.528 | 13.666 | |||
| BRem2 | Log transform of fixed remuneration per director (€) | Overall | 11.649 | 1.047 | 7.041 | 14.501 |
| Between | 1.045 | 7.118 | 13.364 | |||
| Within | 0.358 | 9.115 | 13.644 | |||
| BRem3 | Total remuneration as a share of net income | Overall | 0.039 | 0.166 | −0.494 | 0.840 |
| Between | 0.122 | −0.494 | 0.442 | |||
| Within | 0.130 | −0.606 | 0.841 | |||
| BRem4 | Variable remuneration as a share of net income | Overall | 0.015 | 0.048 | −0.126 | 0.205 |
| Between | 0.037 | −0.126 | 0.180 | |||
| Within | 0.035 | −0.213 | 0.227 | |||
| Panel D: Measures of board and firm features | ||||||
| BOwn | Percentage of equity capital owned by board | Overall | 0.206 | 0.234 | 0.000 | 0.893 |
| Between | 0.226 | 0.000 | 0.839 | |||
| Within | 0.073 | −0.180 | 0.675 | |||
| BIndep | Percentage of independent directors | Overall | 0.413 | 0.168 | 0.000 | 1.000 |
| Between | 0.144 | 0.125 | 0.759 | |||
| Within | 0.083 | −0.087 | 0.778 | |||
| BSize | Number of board members | Overall | 10.262 | 3.206 | 4.000 | 19.000 |
| Between | 3.170 | 4.000 | 17.500 | |||
| Within | 0.832 | 7.662 | 14.262 | |||
| BExec | Percentage of executive directors | Overall | 0.1651 | 0.110 | 0.000 | 0.667 |
| Between | 0.101 | 0.000 | 0.540 | |||
| Within | 0.046 | 0.005 | 0.405 | |||
| Size | Log transformation of company’s total assets | Overall | 20.992 | 2.090 | 15.790 | 25.541 |
| Between | 2.109 | 16.424 | 25.502 | |||
| Within | 0.210 | 19.830 | 22.090 | |||
| Risk | Altman’s (1968) Z-score index | Overall | 2.456 | 2.772 | −8.966 | 16.554 |
| Between | 2.760 | −6.173 | 13.806 | |||
| Within | 0.843 | −1.891 | 7.360 | |||
| ROA | Net income over total assets | Overall | 0.032 | 0.068 | −0.192 | 0.212 |
| Between | 0.065 | −0.192 | 0.212 | |||
| Within | 0.037 | −0.170 | 0.234 | |||
| Lev | Short- and long-term debt over total assets | Overall | 0.289 | 0.181 | 0.000 | 0.825 |
| Between | 0.185 | 0.000 | 0.825 | |||
| Within | 0.060 | −0.054 | 0.654 | |||
| GO | Growth opportunities | Overall | 1.210 | 1.018 | 0.059 | 7.730 |
| Between | 1.012 | 0.094 | 6.357 | |||
| Within | 0.347 | −1.325 | 4.375 | |||
| Variable | Description | Mean | SD | Min | Max | |
|---|---|---|---|---|---|---|
| Panel A: Earnings-quality measures | ||||||
| EQ1 | Country rank of earnings quality, defined by StarMine | Overall | 0.452 | 0.288 | 0.010 | 1.000 |
| Between | 0.224 | 0.010 | 0.888 | |||
| Within | 0.199 | −0.188 | 0.967 | |||
| EQ2 | Accrual measure of earnings quality, defined by StarMine | Overall | 0.503 | 0.263 | 0.010 | 1.000 |
| Between | 0.172 | 0.040 | 0.940 | |||
| Within | 0.218 | −0.077 | 1.106 | |||
| EQ3 | Cash flow measure of earnings quality, defined by StarMine | Overall | 0.558 | 0.222 | 0.040 | 1.000 |
| Between | 0.197 | 0.090 | 1.000 | |||
| Within | 0.127 | 0.045 | 1.060 | |||
| EQ4 | Operating-efficiency measure of earnings quality, defined by StarMine | Overall | 0.548 | 0.263 | 0.010 | 1.000 |
| Between | 0.230 | 0.055 | 0.978 | |||
| Within | 0.152 | −0.030 | 1.125 | |||
| EM | Earnings-management measure based on modified Jones’s model | Overall | −0.061 | 0.108 | −0.941 | 0.000 |
| Between | 0.115 | −0.941 | 0.000 | |||
| Within | 0.075 | −0.760 | 0.324 | |||
| Panel B: Board-gender-diversity measures | ||||||
| GD1 | Percentage of female directors | Overall | 0.159 | 0.122 | 0.000 | 0.571 |
| Between | 0.105 | 0.000 | 0.514 | |||
| Within | 0.061 | −0.008 | 0.436 | |||
| GD2 | Overall | 0.238 | 0.152 | 0.000 | 0.500 | |
| Between | 0.134 | 0.000 | 0.487 | |||
| Within | 0.077 | 0.019 | 0.538 | |||
| GD3 | Overall | 0.374 | 0.218 | 0.000 | 0.693 | |
| Between | 0.193 | 0.000 | 0.680 | |||
| Within | 0.113 | 0.023 | 0.823 | |||
| Panel C: Board-remuneration measures | ||||||
| BRem1 | Log transform of total remuneration per director (€) | Overall | 12.119 | 1.184 | 7.041 | 14.630 |
| Between | 1.227 | 7.118 | 14.057 | |||
| Within | 0.358 | 10.528 | 13.666 | |||
| BRem2 | Log transform of fixed remuneration per director (€) | Overall | 11.649 | 1.047 | 7.041 | 14.501 |
| Between | 1.045 | 7.118 | 13.364 | |||
| Within | 0.358 | 9.115 | 13.644 | |||
| BRem3 | Total remuneration as a share of net income | Overall | 0.039 | 0.166 | −0.494 | 0.840 |
| Between | 0.122 | −0.494 | 0.442 | |||
| Within | 0.130 | −0.606 | 0.841 | |||
| BRem4 | Variable remuneration as a share of net income | Overall | 0.015 | 0.048 | −0.126 | 0.205 |
| Between | 0.037 | −0.126 | 0.180 | |||
| Within | 0.035 | −0.213 | 0.227 | |||
| Panel D: Measures of board and firm features | ||||||
| BOwn | Percentage of equity capital owned by board | Overall | 0.206 | 0.234 | 0.000 | 0.893 |
| Between | 0.226 | 0.000 | 0.839 | |||
| Within | 0.073 | −0.180 | 0.675 | |||
| BIndep | Percentage of independent directors | Overall | 0.413 | 0.168 | 0.000 | 1.000 |
| Between | 0.144 | 0.125 | 0.759 | |||
| Within | 0.083 | −0.087 | 0.778 | |||
| BSize | Number of board members | Overall | 10.262 | 3.206 | 4.000 | 19.000 |
| Between | 3.170 | 4.000 | 17.500 | |||
| Within | 0.832 | 7.662 | 14.262 | |||
| BExec | Percentage of executive directors | Overall | 0.1651 | 0.110 | 0.000 | 0.667 |
| Between | 0.101 | 0.000 | 0.540 | |||
| Within | 0.046 | 0.005 | 0.405 | |||
| Size | Log transformation of company’s total assets | Overall | 20.992 | 2.090 | 15.790 | 25.541 |
| Between | 2.109 | 16.424 | 25.502 | |||
| Within | 0.210 | 19.830 | 22.090 | |||
| Risk | Overall | 2.456 | 2.772 | −8.966 | 16.554 | |
| Between | 2.760 | −6.173 | 13.806 | |||
| Within | 0.843 | −1.891 | 7.360 | |||
| ROA | Net income over total assets | Overall | 0.032 | 0.068 | −0.192 | 0.212 |
| Between | 0.065 | −0.192 | 0.212 | |||
| Within | 0.037 | −0.170 | 0.234 | |||
| Lev | Short- and long-term debt over total assets | Overall | 0.289 | 0.181 | 0.000 | 0.825 |
| Between | 0.185 | 0.000 | 0.825 | |||
| Within | 0.060 | −0.054 | 0.654 | |||
| GO | Growth opportunities | Overall | 1.210 | 1.018 | 0.059 | 7.730 |
| Between | 1.012 | 0.094 | 6.357 | |||
| Within | 0.347 | −1.325 | 4.375 | |||
Notes:
This table shows the mean, the standard deviation and the minimum and maximum for the total sample variables, differentiating between overall, between and within measures of the statistics (e.g. standard deviation, minimum and maximum). EQ1 to EQ4 as well as EM are the alternative metrics of earnings quality, which are the dependent variables in the main study. These metrics correspond to the measures of earnings quality developed by StarMine, based on a percentile ranking from 0 to 1 of stocks based on sustainability of earnings, with 1 representing the highest rank. These metrics are classified as the overall measure of earnings quality (EQ1), earnings quality corresponding to an accruals measure (EQ2), a cash flow measure (EQ3) and an operating-efficiency measure (EQ4). EM is the measure of earnings quality based on discretionary accruals used in the robustness analyses. By construction, EM takes negative values, with higher values indicating better earnings quality. GD1 to GD3 are the board’s gender-diversity alternative metrics for our first independent variable. BRem1 to BRem4 are the measures of remuneration of the board of directors, related to the hypotheses regarding the directors’ compensation. BOwn is the percentage of the equity capital owned by the board of directors, BIndep is the percentage of independent board members, BSize is the number of directors on the board of directors and BExec is the percentage of executive directors. Size is firm size as the logarithm of total assets. Risk measures the default risk based on Altman’s (1968) Z-score. ROA measures the return on assets, computed as net income over total assets. Lev measures the leverage ratio, defined as the sum of short- and long-term debt over total assets. Finally, GO is a metric of growth opportunities, defined as total assets less total common equity plus the company’s market capitalization, all scaled by total assets.
***Significance of the t test at 1%
For board remuneration (BRem1), we estimate that the average annual amount earned per director is about €183,000. Of this amount, approximately €115,000 come from fixed components (BRem2) and the remaining euros (less than 40%) come from variable components. The total remuneration equals 3.9% of the company’s net annual income (BRem3), whereas the variable remuneration amounts to 1.5% of net income (BRem4).
Examining other board features, a remarkably high proportion (20.6%) of equity capital is owned by boards (BOwn). Additionally, the average board size is 10.3 members (BSize), of whom about 41.3% are independent directors (BIndep) and 16.5% are executive directors (BExec).
Looking at firm-level control variables, the average ROA is 3.2% and the debt ratio is 28.9% of assets (Lev). Regarding the firm’s risk, the Altman (1968) Z-score metric (Risk) indicates that companies are almost in the safety zone [14]. The average growth-opportunity value (GO) indicates that Spanish companies do account for future growth opportunities because the metric is greater than 1, in line with results in previous empirical works (Adam and Goyal, 2008) [15].
4.2 Multivariate analysis
Table 1 shows a consistent and statistically significant positive impact of gender diversity (GD1) on earnings quality, implying that the more gender-balanced the board is, the more transparent the financial reports disclosed by the company and the more informative its earnings. Previous empirical research finds that female participation on boards of directors is positively associated with good governance (Adams and Ferreira, 2009; Erhardt et al., 2003). Our results lead us to postulate that, in general, a gender-balanced board takes advantage of the characteristics of women and men while enhancing transparency and mitigating unethical behavior by the board. Thus, although Spanish boards’ gender diversity is still far from the European-wide average recommendations, gender diversity is value relevant. Hence, as shown by Orazalin (2020), gender-balanced boards improve the transparency and earnings quality.
Gender diversity and board remuneration’s effects on earnings quality
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| Variables | EQ1 | EQ1 | EQ1 | EQ1 |
| GD1 | 0.567** (0.234) | 0.523** (0.228) | 0.485** (0.229) | 0.470** (0.222) |
| BRem1 | 0.839*** (0.315) | |||
| BRem12 | −0.032** (0.013) | |||
| BRem2 | 0.551** (0.242) | |||
| BRem22 | −0.021** (0.010) | |||
| BRem3 | 0.069 (0.096) | |||
| BRem32 | −0.246* (0.129) | |||
| BRem4 | 0.512 (0.396) | |||
| BRem42 | −5.726** (2.536) | |||
| Extrema point | 13.037 | 12.851 | 0.140 | 0.045 |
| Lind−Mehlum | 1.860 | 1.707 | 1.520 | 2.020 |
| p-value | (0.032) | (0.042) | (0.064) | (0.022) |
| BOwn | −0.308** (−2.16) | −0.2960** (−2.081) | −0.268** (−2.018) | −0.281** (−2.148) |
| BIndep | −0.036 (−0.245) | −0.023 (−0.1550) | 0.011 (0.071) | 0.032 (0.212) |
| BSize | −0.016 (−0.942) | −0.022 (−1.299) | −0.015 (−0.841) | −0.014 (−0.789) |
| BExec | 0.075 (0.385) | 0.056 (0.2897) | 0.073 (0.368) | 0.089 (0.445) |
| Size | −0.097 (−1.425) | −0.083 (−1.512) | −0.057 (−0.858) | −0.045 (−0.710) |
| Risk | 0.009 (0.569) | 0.010 (0.637) | 0.001 (0.059) | −0.000 (−0.009) |
| ROA | 1.138*** (3.248) | 1.156*** (3.247) | 1.075*** (3.092) | 1.162*** (3.312) |
| Lev | −0.240 (−0.825) | −0.298 (−1.063) | −0.332 (−1.164) | −0.341 (−1.137) |
| GO | −0.060 (−1.445) | −0.052 (−1.295) | −0.048 (−1.444) | −0.051 (−1.487) |
| Governance1 | −0.010 (−0.742) | −0.009 (−0.715) | −0.013 (−0.978) | −0.014 (−1.140) |
| Governance2 | 0.004 (0.204) | 0.005 (0.253) | 0.008 (0.415) | 0.004 (0.274) |
| Governance3 | −0.035 (−0.849) | −0.027 (−0.659) | −0.035 (−0.866) | −0.032 (−0.833) |
| Observations | 486 | 482 | 485 | 491 |
| Mean VIF | 1.566 | 1.109 | 1.318 | 2.107 |
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| Variables | EQ1 | EQ1 | EQ1 | EQ1 |
| GD1 | 0.567** (0.234) | 0.523** (0.228) | 0.485** (0.229) | 0.470** (0.222) |
| BRem1 | 0.839 | |||
| BRem12 | −0.032** (0.013) | |||
| BRem2 | 0.551** (0.242) | |||
| BRem22 | −0.021** (0.010) | |||
| BRem3 | 0.069 (0.096) | |||
| BRem32 | −0.246* (0.129) | |||
| BRem4 | 0.512 (0.396) | |||
| BRem42 | −5.726** (2.536) | |||
| Extrema point | 13.037 | 12.851 | 0.140 | 0.045 |
| Lind−Mehlum | 1.860 | 1.707 | 1.520 | 2.020 |
| p-value | (0.032) | (0.042) | (0.064) | (0.022) |
| BOwn | −0.308** (−2.16) | −0.2960** (−2.081) | −0.268** (−2.018) | −0.281** (−2.148) |
| BIndep | −0.036 (−0.245) | −0.023 (−0.1550) | 0.011 (0.071) | 0.032 (0.212) |
| BSize | −0.016 (−0.942) | −0.022 (−1.299) | −0.015 (−0.841) | −0.014 (−0.789) |
| BExec | 0.075 (0.385) | 0.056 (0.2897) | 0.073 (0.368) | 0.089 (0.445) |
| Size | −0.097 (−1.425) | −0.083 (−1.512) | −0.057 (−0.858) | −0.045 (−0.710) |
| Risk | 0.009 (0.569) | 0.010 (0.637) | 0.001 (0.059) | −0.000 (−0.009) |
| ROA | 1.138 | 1.156 | 1.075 | 1.162 |
| Lev | −0.240 (−0.825) | −0.298 (−1.063) | −0.332 (−1.164) | −0.341 (−1.137) |
| GO | −0.060 (−1.445) | −0.052 (−1.295) | −0.048 (−1.444) | −0.051 (−1.487) |
| Governance1 | −0.010 (−0.742) | −0.009 (−0.715) | −0.013 (−0.978) | −0.014 (−1.140) |
| Governance2 | 0.004 (0.204) | 0.005 (0.253) | 0.008 (0.415) | 0.004 (0.274) |
| Governance3 | −0.035 (−0.849) | −0.027 (−0.659) | −0.035 (−0.866) | −0.032 (−0.833) |
| Observations | 486 | 482 | 485 | 491 |
| Mean VIF | 1.566 | 1.109 | 1.318 | 2.107 |
Notes:
Regressions with Tobit semiparametric estimator with fixed effects at the firm and industry levels following Honoré (1992). Where EQ1 is earnings quality. GD1 is a measure of gender diversity. BRem1, BRem2, BRem3 and BRem4 are the measures of boards of directors’ remuneration. Control variables are the percentage of equity capital owned by the board of directors (BOwn), the percentage of independent board members (BIndep), the number of directors on the board (BSize), the percentage of executive directors (BExec), firm size (Size), default risk based on Altman’s (1968) Z-score (Risk), return on assets (ROA), leverage ratio (Lev), growth opportunities (GO) and factor variables from the Cluster-Focused Principal Component Factoring technique (Governance1, Governance2 and Governance3) that capture key attributes of the Unified Good Governance Code. The table also shows the extrema point (or threshold) for each regression using the alternative remuneration measures tested with Lind and Mehlum’s (2010) contrast. Standard errors are in parentheses.
***, ** and * simply statistical significance at 1, 5 and 10%, respectively
Figure 1 plots the point estimates of the alternative versions of board gender diversity (GD1, GD2 and GD3) against the various measures of earnings quality (EQ1, EQ2, EQ3, EQ4 and EM). The positive relationship between gender diversity and earnings quality remains statistically significant and consistent in magnitude for all versions of earnings quality against all forms of gender diversity, except for EQ3 (earnings quality measure based on cash flows) which is not statistically significant at a 5% level for GD1, GD2 or GD3. This provides robust evidence in support of H1. This result reinforces Orazalin’s (2020) finding that gender parity in board composition improves the quality of accounting information.
The results concerning board compensation indicate a nonmonotonic relationship between compensation and earnings quality. For instance, Model (1) of Table 1 – analyzing remuneration per board member (BRem1) – shows that the estimated coefficients of BRem1 and BRem12 are positive and negative, respectively. This implies a diminishing marginal effect of total remuneration per director on earnings quality, suggesting a global maximum exists. The Lind and Mehlum (2010) test, displayed below the estimated extrema points in Table 1, does not support a monotonic relationship between BRem1 and EQ1, suggesting a threshold exists near €459,000 per director [16]. Because the mean value of BRem1 is less than the threshold, this finding suggests that the average Spanish company behaves consistently with the incentive hypothesis, meaning that board members closely monitor managerial behavior as compensation increases. This finding also indicates that several firms operate consistently with the perverse-incentive hypothesis.
Figure 2 displays the optimization of earnings quality with respect to compensation per director (BRem1), as derived in Model (1) of Table 1. The figure supports the notion that board remuneration has an inverse U-shaped relationship with earning quality. At low remuneration, increases in director compensation improve the quality of earnings. But starting near €459,000, firms (21.19% of them) begin to operate consistently with the perverse-incentive hypothesis.
Optimization of earnings quality with respect to board remuneration
Table A4 in Appendix 4 expands on Table 1 by alternating the gender-diversity measure and the board-remuneration measure used in each specification. The positive influence of gender parity on earnings quality (EQ1) does not appear to be sensitive to the variable definition. In addition, the concave relationship between board remuneration and earnings quality does not depend on the measure of board remuneration. Indeed, the four metrics of board remuneration used in the estimations indicate that EQ1 increases until it achieves certain maximum level, after which the earnings quality deteriorates as the remuneration keeps increasing, as supported by the Lind–Melhum test. For instance, the average extrema point that represents the level at which additional compensation damages financial transparency in the various model specifications for BRem1 remains comparable to the one estimated in Table 1. In addition, it is observed that earnings quality improves as long as the log transformation of fixed board remuneration (BRem2) is not beyond the average of about €420,000. Models (3), (4), (7) and (8) of Table A4 also support the concave relationship as tested with the Lind–Melhum contrast located below the estimated extrema points [17]. Findings indicate that increasing board compensation results in improved financial transparency until it reaches an average of 13.10% of the firm’s net income (BRem3), at which point additional board remuneration begins to damage financial transparency. However, the average board-compensation package of a Spanish company is about 3.90% of net annual income [18]. This corroborates the above finding that most of the Spanish corporate sector operates consistently with the incentive hypothesis. A similar relationship is observed when we consider BRem4.
Overall-earnings-quality regression estimates (EQ1)
| Variables | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) |
|---|---|---|---|---|---|---|---|---|
| GD2 | 0.3841** (2.1008) | 0.3563** (2.0019) | 0.3285* (1.7886) | 0.3184* (1.8070) | ||||
| GD3 | 0.2368* (1.8719) | 0.2177* (1.7482) | 0.1975 (1.5575) | 0.1902 (1.5001) | ||||
| BRem1 | 0.7972** (2.5097) | 0.7868** (2.4912) | ||||||
| BRem12 | −0.0306** (−2.3449) | −0.0301** (−2.3213) | ||||||
| BRem2 | 0.5302** (2.2230) | 0.5190** (2.1908) | ||||||
| BRem22 | −0.0205** (−1.9862) | −0.0200* (−1.9488) | ||||||
| BRem3 | 0.0631 (0.6732) | 0.0611 (0.6381) | ||||||
| BRem32 | −0.2385* (−1.8401) | −0.2357* (−1.8049) | ||||||
| BRem4 | 0.4814 (1.1984) | 0.4635 (1.0820) | ||||||
| BRem42 | −5.5819** (−2.1643) | −5.5156** (−2.0337) | ||||||
| Extrema point | 13.0371 | 12.9248 | 0.1324 | 0.0431 | 13.0548 | 12.9848 | 0.1295 | 0.0420 |
| Lind-Melhum | 1.7800 | 0.9300 | 1.4700 | 1.9000 | 1.7500 | 0.8800 | 1.4100 | 1.7600 |
| p-value | (0.0382) | (0.0622) | (0.0714) | (0.0288) | (0.0407) | (0.0901) | (0.0794) | (0.0396) |
| BOwn | −0.3037** (−2.1046) | −0.2927** (−2.0258) | −0.2670** (−1.9762) | −0.2798** (−2.1017) | −0.3015** (−2.0887) | −0.2902** (−2.0123) | −0.2655** (−1.9776) | −0.2786** (−2.0858) |
| BIndep | −0.0148 (−0.1037) | −0.0039 (−0.0264) | 0.0278 (0.1812) | 0.0482 (0.3219) | −0.0000 (−0.0003) | 0.0106 (0.0739) | 0.0420 (0.2752) | 0.0614 (0.4100) |
| BSize | −0.0189 (−1.0774) | −0.0241 (−1.4036) | −0.0171 (−0.9430) | −0.0154 (−0.8709) | −0.0191 (−1.0935) | −0.0242 (−1.4096) | −0.0172 (−0.9514) | −0.0157 (−0.8817) |
| BExec | 0.0676 (0.3363) | 0.0479 (0.2401) | 0.0668 (0.3291) | 0.0828 (0.3894) | 0.0629 (0.3086) | 0.0435 (0.2161) | 0.0630 (0.3172) | 0.0795 (0.3674) |
| Size | −0.0814 (−1.1717) | −0.0708 (−1.2565) | −0.0469 (−0.6643) | −0.0350 (−0.5431) | −0.0751 (−1.0699) | −0.0648 (−1.1399) | −0.0422 (−0.5931) | −0.0306 (−0.4602) |
| Risk | 0.0083 (0.5068) | 0.0092 (0.5760) | 0.0004 (0.0262) | −0.0007 (−0.0426) | 0.0076 (0.4674) | 0.0085 (0.5362) | −0.0001 (−0.0058) | −0.0012 (−0.0742) |
| ROA | 1.1363*** (3.2170) | 1.1614*** (3.2260) | 1.0766*** (3.0602) | 1.1636*** (3.3368) | 1.1363*** (3.2068) | 1.1658*** (3.2484) | 1.0793*** (3.0540) | 1.1666*** (3.2568) |
| Lev | −0.2769 (−0.9738) | −0.3208 (−1.1526) | −0.3647 (−1.2612) | −0.3656 (−1.2234) | −0.2855 (−0.9993) | −0.3257 (−1.1807) | −0.3718 (−1.2872) | −0.3741 (−1.2474) |
| GO | −0.0590 (−1.4298) | −0.0511 (−1.2679) | −0.0477 (−1.4365) | −0.0509 (−1.4792) | −0.0573 (−1.3816) | −0.0489 (−1.2109) | −0.0463 (−1.3864) | −0.0493 (−1.4154) |
| Governance1 | −0.0105 (−0.8159) | −0.0100 (−0.7877) | −0.0137 (−1.0379) | −0.0152 (−1.1924) | −0.0119 (−0.9284) | −0.0113 (−0.8998) | −0.0151 (−1.1615) | −0.0165 (−1.2994) |
| Governance2 | 0.0043 (0.2116) | 0.0047 (0.2639) | 0.0084 (0.4182) | 0.0045 (0.2815) | 0.0045 (0.2198) | 0.0049 (0.2784) | 0.0085 (0.4262) | 0.0047 (0.2930) |
| Governance3 | −0.0372 (−0.9186) | −0.0293 (−0.7269) | −0.0365 (−0.9154) | −0.0338 (−0.8991) | −0.0386 (−0.9419) | −0.0308 (−0.7577) | −0.0376 (−0.9508) | −0.0349 (−0.9213) |
| Observations | 486 | 482 | 485 | 491 | 486 | 482 | 485 | 491 |
| Chi2 | 53.9100 | 61.4900 | 59.2700 | 58.3200 | 53.5000 | 61.5200 | 58.5200 | 56.7100 |
| p-value | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) |
| Year/Ind FE | YES | YES | YES | YES | YES | YES | YES | YES |
| Variables | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) |
|---|---|---|---|---|---|---|---|---|
| GD2 | 0.3841** (2.1008) | 0.3563** (2.0019) | 0.3285* (1.7886) | 0.3184* (1.8070) | ||||
| GD3 | 0.2368* (1.8719) | 0.2177* (1.7482) | 0.1975 (1.5575) | 0.1902 (1.5001) | ||||
| BRem1 | 0.7972** (2.5097) | 0.7868** (2.4912) | ||||||
| BRem12 | −0.0306** (−2.3449) | −0.0301** (−2.3213) | ||||||
| BRem2 | 0.5302** (2.2230) | 0.5190** (2.1908) | ||||||
| BRem22 | −0.0205** (−1.9862) | −0.0200* (−1.9488) | ||||||
| BRem3 | 0.0631 (0.6732) | 0.0611 (0.6381) | ||||||
| BRem32 | −0.2385* (−1.8401) | −0.2357* (−1.8049) | ||||||
| BRem4 | 0.4814 (1.1984) | 0.4635 (1.0820) | ||||||
| BRem42 | −5.5819** (−2.1643) | −5.5156** (−2.0337) | ||||||
| Extrema point | 13.0371 | 12.9248 | 0.1324 | 0.0431 | 13.0548 | 12.9848 | 0.1295 | 0.0420 |
| Lind-Melhum | 1.7800 | 0.9300 | 1.4700 | 1.9000 | 1.7500 | 0.8800 | 1.4100 | 1.7600 |
| p-value | (0.0382) | (0.0622) | (0.0714) | (0.0288) | (0.0407) | (0.0901) | (0.0794) | (0.0396) |
| BOwn | −0.3037** (−2.1046) | −0.2927** (−2.0258) | −0.2670** (−1.9762) | −0.2798** (−2.1017) | −0.3015** (−2.0887) | −0.2902** (−2.0123) | −0.2655** (−1.9776) | −0.2786** (−2.0858) |
| BIndep | −0.0148 (−0.1037) | −0.0039 (−0.0264) | 0.0278 (0.1812) | 0.0482 (0.3219) | −0.0000 (−0.0003) | 0.0106 (0.0739) | 0.0420 (0.2752) | 0.0614 (0.4100) |
| BSize | −0.0189 (−1.0774) | −0.0241 (−1.4036) | −0.0171 (−0.9430) | −0.0154 (−0.8709) | −0.0191 (−1.0935) | −0.0242 (−1.4096) | −0.0172 (−0.9514) | −0.0157 (−0.8817) |
| BExec | 0.0676 (0.3363) | 0.0479 (0.2401) | 0.0668 (0.3291) | 0.0828 (0.3894) | 0.0629 (0.3086) | 0.0435 (0.2161) | 0.0630 (0.3172) | 0.0795 (0.3674) |
| Size | −0.0814 (−1.1717) | −0.0708 (−1.2565) | −0.0469 (−0.6643) | −0.0350 (−0.5431) | −0.0751 (−1.0699) | −0.0648 (−1.1399) | −0.0422 (−0.5931) | −0.0306 (−0.4602) |
| Risk | 0.0083 (0.5068) | 0.0092 (0.5760) | 0.0004 (0.0262) | −0.0007 (−0.0426) | 0.0076 (0.4674) | 0.0085 (0.5362) | −0.0001 (−0.0058) | −0.0012 (−0.0742) |
| ROA | 1.1363 | 1.1614 | 1.0766 | 1.1636 | 1.1363 | 1.1658 | 1.0793 | 1.1666 |
| Lev | −0.2769 (−0.9738) | −0.3208 (−1.1526) | −0.3647 (−1.2612) | −0.3656 (−1.2234) | −0.2855 (−0.9993) | −0.3257 (−1.1807) | −0.3718 (−1.2872) | −0.3741 (−1.2474) |
| GO | −0.0590 (−1.4298) | −0.0511 (−1.2679) | −0.0477 (−1.4365) | −0.0509 (−1.4792) | −0.0573 (−1.3816) | −0.0489 (−1.2109) | −0.0463 (−1.3864) | −0.0493 (−1.4154) |
| Governance1 | −0.0105 (−0.8159) | −0.0100 (−0.7877) | −0.0137 (−1.0379) | −0.0152 (−1.1924) | −0.0119 (−0.9284) | −0.0113 (−0.8998) | −0.0151 (−1.1615) | −0.0165 (−1.2994) |
| Governance2 | 0.0043 (0.2116) | 0.0047 (0.2639) | 0.0084 (0.4182) | 0.0045 (0.2815) | 0.0045 (0.2198) | 0.0049 (0.2784) | 0.0085 (0.4262) | 0.0047 (0.2930) |
| Governance3 | −0.0372 (−0.9186) | −0.0293 (−0.7269) | −0.0365 (−0.9154) | −0.0338 (−0.8991) | −0.0386 (−0.9419) | −0.0308 (−0.7577) | −0.0376 (−0.9508) | −0.0349 (−0.9213) |
| Observations | 486 | 482 | 485 | 491 | 486 | 482 | 485 | 491 |
| Chi2 | 53.9100 | 61.4900 | 59.2700 | 58.3200 | 53.5000 | 61.5200 | 58.5200 | 56.7100 |
| p-value | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) |
| Year/Ind FE | YES | YES | YES | YES | YES | YES | YES | YES |
Notes:
The table shows the regression estimates with Tobit semiparametric estimator with fixed effects at the firm and industry levels according to Honoré (1992). EQ1 is used as the dependent variable. GD2 and GD3 are the alternative gender-diversity metrics for our first independent variable. BRem1 to BRem4 are the measures of remuneration of the board of directors, related to the hypotheses regarding the directors’ compensation. BOwn is the percentage of equity capital owned by the board of directors, BIndep is the percentage of independent board members, BSize is the number of directors on the board of directors and BExec is the percentage of executive directors. Size is firm size as the logarithm of total assets. Risk measures default risk based on Altman’s (1968) Z-score. ROA measures the return on assets, computed as net income over total assets. Lev measures the leverage ratio, defined as the sum of short- and long-term debt over total assets. GO is a metric of growth opportunities, defined as total assets less total common equity plus the company’s market capitalization, all scaled by total assets. Finally, factor variables from the Cluster-Focused Principal Component Factoring technique (Governance1, Governance2 and Governance3) that capture key attributes of the Unified Good Governance Code were also included. The table also shows the extrema point (or threshold) for each regression using the alternative BRem measures, which are tested with Lind and Mehlum’s (2010) contrast. z-statistics in parentheses.
***, ** and * simply significance at 1, 5 and 10%, respectively
4.3 Robustness and endogeneity concerns
To test the robustness of our main findings, we reestimate the previous results by alternating the measures of earnings quality (EQ2, EQ3 and EQ4) and varying the metrics for gender parity (GD1, GD2 and GD3) and board remuneration (BRem1, BRem2, BRem3 and BRem4) in the specified model. The results are shown in Appendix 4.
Robustness results displayed in Table A5 use as dependent variables the accrual measure (EQ2), cash flow measure (EQ3) and operating-efficiency measure (EQ4) of earnings quality. Again, we observe that board gender diversity increases the quality of earnings, but only when considering the EQ2 measure.
Earnings-quality regression estimates (EQ2, EQ3 and EQ4)
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | |
|---|---|---|---|---|---|---|---|---|---|
| Variables | EQ2 | EQ2 | EQ2 | EQ3 | EQ3 | EQ3 | EQ4 | EQ4 | EQ4 |
| GD1 | 0.5739*** (2.9973) | 0.0090 (0.0555) | 0.2595 (1.5272) | ||||||
| GD2 | 0.3687** (2.4613) | −0.0151 (−0.1144) | 0.1336 (1.0010) | ||||||
| GD3 | 0.2242** (2.2413) | −0.0240 (−0.2618) | 0.0856 (0.9487) | ||||||
| BRem1 | 0.5627* (1.9474) | 0.5293* (1.8240) | 0.5203* (1.7964) | 0.4270** (2.3376) | 0.4247** (2.3267) | 0.4235** (2.3174) | 0.3536* (1.9040) | 0.3331* (1.7827) | 0.3310* (1.7715) |
| BRem12 | −0.0215* (−1.7425) | −0.0202 (−1.6332) | −0.0198 (−1.6041) | −0.0171** (−2.1947) | −0.0170** (−2.1837) | −0.0170** (−2.1745) | −0.0130* (−1.7068) | −0.0122 (−1.5917) | −0.0121 (−1.5791) |
| Extrema point | 13.0762 | 13.0934 | 13.1354 | 12.4736 | 12.4776 | 12.4799 | 13.6119 | 13.6762 | 13.6966 |
| Lind–Melhum | 1.6200 | 1.1590 | 1.1100 | 1.7700 | 1.7600 | 1.7600 | 1.0600 | 0.9700 | 0.9600 |
| p-value | (0.081) | (0.098) | (0.135) | (0.038) | (0.039) | (0.040) | (0.144) | (0.165) | (0.169) |
| BOwn | −0.1243 (−0.7086) | −0.1199 (−0.6752) | −0.1181 (−0.6647) | −0.0263 (−0.2876) | −0.0242 (−0.2660) | −0.0222 (−0.2433) | −0.2121* (−1.8522) | −0.2066* (−1.7907) | −0.2065* (−1.7829) |
| BIndep | −0.0632 (−0.4484) | −0.0399 (−0.2840) | −0.0259 (−0.1849) | 0.0245 (0.2372) | 0.0297 (0.2976) | 0.0336 (0.3411) | 0.0279 (0.2374) | 0.0478 (0.4225) | 0.0523 (0.4679) |
| BSize | −0.0133 (−0.8696) | −0.0155 (−0.9899) | −0.0155 (−0.9895) | −0.0033 (−0.2991) | −0.0033 (−0.3012) | −0.0032 (−0.2969) | −0.0072 (−0.6915) | −0.0081 (−0.7704) | −0.0081 (−0.7737) |
| BExec | −0.2047 (−0.9626) | −0.2172 (−1.0098) | −0.2228 (−1.0560) | −0.1120 (−0.7449) | −0.1150 (−0.7598) | −0.1176 (−0.7762) | 0.1813 (1.0419) | 0.1729 (0.9778) | 0.1718 (0.9672) |
| Size | −0.0664 (−0.9909) | −0.0529 (−0.7626) | −0.0479 (−0.6837) | 0.0250 (0.5644) | 0.0265 (0.6012) | 0.0274 (0.6213) | −0.0086 (−0.1595) | 0.0005 (0.0083) | 0.0020 (0.0364) |
| Risk | −0.0049 (−0.2646) | −0.0059 (−0.3197) | −0.0064 (−0.3449) | 0.0068 (0.7811) | 0.0069 (0.7951) | 0.0071 (0.8172) | 0.0283** (2.4595) | 0.0280** (2.4476) | 0.0278** (2.4340) |
| ROA | −1.2937*** (−3.4952) | −1.2995*** (−3.4791) | −1.2990*** (−3.4587) | 0.8673*** (3.2818) | 0.8700*** (3.2817) | 0.8733*** (3.2912) | 1.8602*** (4.7271) | 1.8696*** (4.7133) | 1.8689*** (4.6842) |
| Lev | −0.3503 (−1.0654) | −0.3856 (−1.1708) | −0.3916 (−1.1818) | 0.0598 (0.3682) | 0.0571 (0.3541) | 0.0555 (0.3427) | 0.0711 (0.3820) | 0.0509 (0.2644) | 0.0479 (0.2461) |
| GO | −0.0972** (−2.1971) | −0.0936** (−2.1322) | −0.0916** (−2.0779) | −0.0227 (−1.1558) | −0.0225 (−1.1458) | −0.0224 (−1.1461) | −0.0148 (−0.5799) | −0.0137 (−0.5373) | −0.0131 (−0.5143) |
| Governance1 | −0.0081 (−0.5933) | −0.0100 (−0.7333) | −0.0115 (−0.8557) | −0.0002 (−0.0257) | −0.0007 (−0.0913) | −0.0011 (−0.1426) | −0.0114 (−1.2825) | −0.0129 (−1.4731) | −0.0133 (−1.5342) |
| Governance2 | −0.0003 (−0.0133) | −0.0001 (−0.0059) | 0.0001 (0.0027) | −0.0040 (−0.3158) | −0.0039 (−0.3075) | −0.0038 (−0.3005) | 0.0064 (0.4881) | 0.0068 (0.5243) | 0.0069 (0.5291) |
| Governance3 | −0.0250 (−0.6881) | −0.0277 (−0.7633) | −0.0290 (−0.8105) | −0.0139 (−0.5688) | −0.0143 (−0.5860) | −0.0147 (−0.6003) | −0.0548* (−1.9220) | −0.0566** (−1.9974) | −0.0570** (−2.0000) |
| Observations | 490 | 490 | 490 | 490 | 473 | 473 | 484 | 484 | 484 |
| Chi2 | 66.670 | 61.200 | 58.050 | 66.670 | 32.660 | 36.360 | 134.770 | 133.120 | 132.220 |
| p-value | (0.000) | (0.000) | (0.000) | (0.000) | (0.005) | (0.004) | (0.000) | (0.000) | (0.000) |
| Year/Ind FE | YES | YES | YES | YES | YES | YES | YES | YES | YES |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | |
|---|---|---|---|---|---|---|---|---|---|
| Variables | EQ2 | EQ2 | EQ2 | EQ3 | EQ3 | EQ3 | EQ4 | EQ4 | EQ4 |
| GD1 | 0.5739 | 0.0090 (0.0555) | 0.2595 (1.5272) | ||||||
| GD2 | 0.3687** (2.4613) | −0.0151 (−0.1144) | 0.1336 (1.0010) | ||||||
| GD3 | 0.2242** (2.2413) | −0.0240 (−0.2618) | 0.0856 (0.9487) | ||||||
| BRem1 | 0.5627* (1.9474) | 0.5293* (1.8240) | 0.5203* (1.7964) | 0.4270** (2.3376) | 0.4247** (2.3267) | 0.4235** (2.3174) | 0.3536* (1.9040) | 0.3331* (1.7827) | 0.3310* (1.7715) |
| BRem12 | −0.0215* (−1.7425) | −0.0202 (−1.6332) | −0.0198 (−1.6041) | −0.0171** (−2.1947) | −0.0170** (−2.1837) | −0.0170** (−2.1745) | −0.0130* (−1.7068) | −0.0122 (−1.5917) | −0.0121 (−1.5791) |
| Extrema point | 13.0762 | 13.0934 | 13.1354 | 12.4736 | 12.4776 | 12.4799 | 13.6119 | 13.6762 | 13.6966 |
| Lind–Melhum | 1.6200 | 1.1590 | 1.1100 | 1.7700 | 1.7600 | 1.7600 | 1.0600 | 0.9700 | 0.9600 |
| p-value | (0.081) | (0.098) | (0.135) | (0.038) | (0.039) | (0.040) | (0.144) | (0.165) | (0.169) |
| BOwn | −0.1243 (−0.7086) | −0.1199 (−0.6752) | −0.1181 (−0.6647) | −0.0263 (−0.2876) | −0.0242 (−0.2660) | −0.0222 (−0.2433) | −0.2121* (−1.8522) | −0.2066* (−1.7907) | −0.2065* (−1.7829) |
| BIndep | −0.0632 (−0.4484) | −0.0399 (−0.2840) | −0.0259 (−0.1849) | 0.0245 (0.2372) | 0.0297 (0.2976) | 0.0336 (0.3411) | 0.0279 (0.2374) | 0.0478 (0.4225) | 0.0523 (0.4679) |
| BSize | −0.0133 (−0.8696) | −0.0155 (−0.9899) | −0.0155 (−0.9895) | −0.0033 (−0.2991) | −0.0033 (−0.3012) | −0.0032 (−0.2969) | −0.0072 (−0.6915) | −0.0081 (−0.7704) | −0.0081 (−0.7737) |
| BExec | −0.2047 (−0.9626) | −0.2172 (−1.0098) | −0.2228 (−1.0560) | −0.1120 (−0.7449) | −0.1150 (−0.7598) | −0.1176 (−0.7762) | 0.1813 (1.0419) | 0.1729 (0.9778) | 0.1718 (0.9672) |
| Size | −0.0664 (−0.9909) | −0.0529 (−0.7626) | −0.0479 (−0.6837) | 0.0250 (0.5644) | 0.0265 (0.6012) | 0.0274 (0.6213) | −0.0086 (−0.1595) | 0.0005 (0.0083) | 0.0020 (0.0364) |
| Risk | −0.0049 (−0.2646) | −0.0059 (−0.3197) | −0.0064 (−0.3449) | 0.0068 (0.7811) | 0.0069 (0.7951) | 0.0071 (0.8172) | 0.0283** (2.4595) | 0.0280** (2.4476) | 0.0278** (2.4340) |
| ROA | −1.2937 | −1.2995 | −1.2990 | 0.8673 | 0.8700 | 0.8733 | 1.8602 | 1.8696 | 1.8689 |
| Lev | −0.3503 (−1.0654) | −0.3856 (−1.1708) | −0.3916 (−1.1818) | 0.0598 (0.3682) | 0.0571 (0.3541) | 0.0555 (0.3427) | 0.0711 (0.3820) | 0.0509 (0.2644) | 0.0479 (0.2461) |
| GO | −0.0972** (−2.1971) | −0.0936** (−2.1322) | −0.0916** (−2.0779) | −0.0227 (−1.1558) | −0.0225 (−1.1458) | −0.0224 (−1.1461) | −0.0148 (−0.5799) | −0.0137 (−0.5373) | −0.0131 (−0.5143) |
| Governance1 | −0.0081 (−0.5933) | −0.0100 (−0.7333) | −0.0115 (−0.8557) | −0.0002 (−0.0257) | −0.0007 (−0.0913) | −0.0011 (−0.1426) | −0.0114 (−1.2825) | −0.0129 (−1.4731) | −0.0133 (−1.5342) |
| Governance2 | −0.0003 (−0.0133) | −0.0001 (−0.0059) | 0.0001 (0.0027) | −0.0040 (−0.3158) | −0.0039 (−0.3075) | −0.0038 (−0.3005) | 0.0064 (0.4881) | 0.0068 (0.5243) | 0.0069 (0.5291) |
| Governance3 | −0.0250 (−0.6881) | −0.0277 (−0.7633) | −0.0290 (−0.8105) | −0.0139 (−0.5688) | −0.0143 (−0.5860) | −0.0147 (−0.6003) | −0.0548* (−1.9220) | −0.0566** (−1.9974) | −0.0570** (−2.0000) |
| Observations | 490 | 490 | 490 | 490 | 473 | 473 | 484 | 484 | 484 |
| Chi2 | 66.670 | 61.200 | 58.050 | 66.670 | 32.660 | 36.360 | 134.770 | 133.120 | 132.220 |
| p-value | (0.000) | (0.000) | (0.000) | (0.000) | (0.005) | (0.004) | (0.000) | (0.000) | (0.000) |
| Year/Ind FE | YES | YES | YES | YES | YES | YES | YES | YES | YES |
Notes:
The table shows the regression estimates with Tobit semiparametric estimator with fixed effects at the firm and industry levels according to Honoré (1992). EQ2, EQ3 and EQ4 are used as the dependent variable. GD1 to GD3 are the alternative gender-diversity metrics for our first independent variable. BRem1 is the measure of remuneration of the board of directors, related to the hypotheses regarding the directors’ compensation. BOwn is the percentage of equity capital owned by the board of directors, BIndep is the percentage of independent board members, BSize is the number of directors on the board of directors and BExec is the percentage of executive directors. Size measures firm size as the logarithm of total assets. Risk measures the default risk based on Altman’s (1968) Z-score. ROA measures the return on assets, computed as net income over total assets. Lev measures the leverage ratio, defined as the sum of short- and long-term debt over total assets. GO is a metric of growth opportunities, defined as total assets less total common equity plus the company’s market capitalization, all scaled by total assets. Finally, factor variables from the Cluster-Focused Principal Component Factoring technique (Governance1, Governance2 and Governance3) that capture key attributes of the Unified Good Governance Code were also included. The table also shows the extrema point (or threshold) for each regression using the alternative BRem1 measure, which is tested with Lind and Mehlum’s (2010) contrast.
z-statistics in parentheses.
***, ** and * simply significance at 1, 5 and 10%, respectively
In addition, we still observe a concave relationship between board compensation (BRem1) and earnings quality when we consider EQ2 or EQ3 as the outcome measure. This relationship shows little sensitivity to our previous findings’ specification.
To address the potential for measurement errors, simultaneity problems and endogeneity problems caused by omitted variables, we test the sensitivity of our model specification by using a two-step GMM-SE with adjusted standard errors for potential heteroskedasticity as proposed by Blundell and Bond (1998) and as reported in Table A6 of Appendix 4 [19]. This table examines EQ1 to EQ4 and the three measures of board gender diversity (GD1, GD2 and GD3) but uses only BRem1 as a measure of board remuneration; we find no differences from the results of the regressions in Tables A4 and A5. Board gender diversity appears consistent with the previously reported findings in Tables A4 and A5. The remuneration variable in this table exhibits the hypothesized relationship when we use two of the alternative metrics of earnings quality (EQ1 and EQ3). We also observe that BRem1 is not statistically significant in the EQ4 model. This sensitivity check indicates a positive impact on quality of financial reporting (EQ1 and EQ3) as total remuneration per director (BRem1) increases, ceteris paribus. This relationship continues up to a certain maximum point, after which earnings quality deteriorates as compensation continues to increase.
Robustness using GMM-SE
| Variables | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| EQ1 | EQ1 | EQ1 | EQ2 | EQ2 | EQ2 | EQ3 | EQ3 | EQ3 | EQ4 | EQ4 | EQ4 | |
| GD1 | 0.4359*** (0.0959) | 0.1818** (0.0844) | 0.1072** (0.0464) | 0.5411*** (0.0746) | ||||||||
| GD2 | 0.3761*** (0.0666) | 0.0457 (0.0717) | 0.0993*** (0.0330) | 0.4608*** (0.0501) | ||||||||
| GD3 | 0.2609*** (0.0355) | 0.0527 (0.0461) | 0.0814*** (0.0247) | 0.2764*** (0.0309) | ||||||||
| BRem1 | 0.4200*** (0.1072) | 0.4880*** (0.1076) | 0.3666*** (0.0987) | −0.1933* (0.1044) | −0.1470 (0.1243) | −0.2429** (0.1160) | 0.5478*** (0.0467) | 0.5362*** (0.0725) | 0.5288*** (0.0484) | 0.0231 (0.0367) | 0.0154 (0.0782) | −0.0333 (0.0600) |
| BRem12 | −0.0156*** (0.0044) | −0.0190*** (0.0044) | −0.0134*** (0.0040) | 0.0086* (0.0044) | 0.0069 (0.0054) | 0.0104** (0.0049) | −0.0221*** (0.0020) | −0.0221*** (0.0031) | −0.0213*** (0.0020) | −0.0006 (0.0017) | −0.0002 (0.0033) | 0.0019 (0.0026) |
| Extrema point | 13.4537 | 12.8690 | 13.7106 | 11.2071 | 10.71618 | 11.7050 | 12.3997 | 12.1111 | 12.4305 | 18.38442a | 45.4338a | 8.5387 |
| Lind–Melhum | 2.410 | 3.610 | 2.080 | 1.640 | 1.030 | 2.030 | 9.430 | 6.630 | 8.460 | – | – | 0.240 |
| p-value | (0.009) | (0.000) | (0.020) | (0.052) | (0.152) | (0.023) | (0.000) | (0.000) | (0.000) | – | – | (0.407) |
| BOwn | −0.2762*** (0.0510) | −0.2959*** (0.0392) | −0.3200*** (0.0410) | −0.2615*** (0.0579) | −0.2341*** (0.0366) | −0.2361*** (0.0350) | −0.0271 (0.0213) | −0.1005*** (0.0262) | −0.0966*** (0.0284) | −0.1377*** (0.0303) | −0.1908*** (0.0288) | −0.2001*** (0.0219) |
| BIndep | −0.2340*** (0.0623) | −0.2120*** (0.0407) | −0.2673*** (0.0578) | −0.0920 (0.0662) | −0.2002*** (0.0396) | −0.1326** (0.0513) | −0.1333*** (0.0249) | −0.1436*** (0.0306) | −0.1397*** (0.0306) | −0.0187 (0.0369) | −0.0833*** (0.0315) | −0.0732*** (0.0266) |
| BSize | −0.0275*** (0.0041) | −0.0318*** (0.0046) | −0.0375*** (0.0047) | −0.0415*** (0.0065) | −0.0491*** (0.0058) | −0.0484*** (0.0056) | −0.0066** (0.0026) | −0.0112*** (0.0028) | −0.0099*** (0.0024) | −0.0075 (0.0047) | −0.0103** (0.0048) | −0.0097** (0.0049) |
| BExec | −0.4452*** (0.0730) | −0.3479*** (0.1068) | −0.5808*** (0.0778) | −0.5735*** (0.0870) | −0.6867*** (0.0724) | −0.6732*** (0.1054) | −0.2605*** (0.0449) | −0.3304*** (0.0583) | −0.2828*** (0.0629) | 0.0729 (0.0661) | 0.1029 (0.0689) | 0.0473 (0.0721) |
| Size | 0.0258** (0.0129) | 0.0277** (0.0113) | 0.0289*** (0.0105) | 0.0427*** (0.0117) | 0.0564*** (0.0112) | 0.0674*** (0.0107) | 0.0240*** (0.0069) | 0.0289*** (0.0062) | 0.0211*** (0.0073) | 0.0186** (0.0092) | 0.0131 (0.0092) | 0.0153* (0.0086) |
| Risk | 0.0018 (0.0071) | 0.0046 (0.0076) | 0.0147** (0.0065) | −0.0069 (0.0091) | 0.0097 (0.0066) | 0.0073 (0.0067) | 0.0259*** (0.0044) | 0.0228*** (0.0048) | 0.0246*** (0.0042) | 0.0395*** (0.0078) | 0.0465*** (0.0069) | 0.0466*** (0.0061) |
| ROA | 0.5212*** (0.1859) | 0.2808* (0.1510) | 0.4281** (0.2005) | −1.4305*** (0.2174) | −1.4987*** (0.2290) | −1.7102*** (0.2199) | 0.6586*** (0.1042) | 0.5401*** (0.1109) | 0.5358*** (0.1130) | 1.1369*** (0.1596) | 1.0189*** (0.1681) | 1.0307*** (0.1558) |
| Lev | −0.4119*** (0.0766) | −0.4900*** (0.0539) | −0.4276*** (0.0531) | −0.4985*** (0.0969) | −0.4249*** (0.1028) | −0.5097*** (0.1082) | 0.0316 (0.0463) | −0.0992** (0.0483) | −0.0032 (0.0651) | 0.1447** (0.0708) | 0.1958** (0.0765) | 0.2189*** (0.0571) |
| GO | 0.0565*** (0.0153) | 0.0531*** (0.0139) | 0.0423*** (0.0148) | −0.0095 (0.0142) | −0.0208 (0.0133) | 0.0024 (0.0161) | −0.0064 (0.0103) | 0.0105 (0.0086) | 0.0023 (0.0095) | −0.0024 (0.0104) | −0.0087 (0.0119) | −0.0045 (0.0107) |
| Governance1 | −0.0210*** (0.0030) | −0.0191*** (0.0036) | −0.0186*** (0.0032) | −0.0172*** (0.0046) | −0.0197*** (0.0037) | −0.0165*** (0.0050) | −0.0064*** (0.0017) | −0.0036* (0.0019) | −0.0050** (0.0021) | −0.0202*** (0.0028) | −0.0193*** (0.0024) | −0.0209*** (0.0025) |
| Governance2 | −0.0007 (0.0082) | 0.0123 (0.0132) | 0.0107 (0.0106) | 0.0148 (0.0152) | 0.0177 (0.0133) | 0.0205 (0.0135) | −0.0097* (0.0049) | −0.0014 (0.0062) | −0.0008 (0.0057) | −0.0223*** (0.0069) | −0.0152** (0.0072) | −0.0133** (0.0062) |
| Governance3 | −0.0026 (0.0168) | 0.0128 (0.0169) | 0.0246 (0.0177) | −0.0257 (0.0190) | −0.0080 (0.0188) | 0.0030 (0.0208) | −0.0470*** (0.0117) | −0.0349*** (0.0124) | −0.0437*** (0.0104) | 0.0202 (0.0139) | 0.0131 (0.0131) | 0.0125 (0.0122) |
| Constant | −2.3971*** (0.6030) | −2.7375*** (0.5903) | −2.0316*** (0.5339) | 1.4667** (0.5731) | 0.9913 (0.6782) | 1.3639** (0.6316) | −3.2354*** (0.2902) | −3.0967*** (0.4198) | −3.0226*** (0.3129) | −0.1898 (0.2442) | −0.0284 (0.3874) | 0.1953 (0.3407) |
| Observations | 486 | 486 | 486 | 490 | 490 | 490 | 473 | 473 | 473 | 484 | 484 | 484 |
| F | 201.360 | 130.340 | 312.720 | 30.450 | 39.970 | 35.640 | 359.720 | 2550.900 | 380.240 | 1877.500 | 520.600 | 869.280 |
| p-value | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) |
| AR(2) | 1.767 | 1.784 | 1.826 | 1.165 | 1.188 | 1.239 | 1.803 | 1.902 | 1.914 | 0.758 | 0.701 | 0.701 |
| p-value | (0.077) | (0.000) | (0.000) | (0.000) | (0.000) | (0.215) | (0.002) | (0.002) | (0.056) | (0.449) | (0.000) | (0.000) |
| Sargan | 118.000 | 119.300 | 122.500 | 121.600 | 127.700 | 131.700 | 155.200 | 157.100 | 159.200 | 155.100 | 152.900 | 152.500 |
| Variables | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| EQ1 | EQ1 | EQ1 | EQ2 | EQ2 | EQ2 | EQ3 | EQ3 | EQ3 | EQ4 | EQ4 | EQ4 | |
| GD1 | 0.4359 | 0.1818** (0.0844) | 0.1072** (0.0464) | 0.5411 | ||||||||
| GD2 | 0.3761 | 0.0457 (0.0717) | 0.0993 | 0.4608 | ||||||||
| GD3 | 0.2609 | 0.0527 (0.0461) | 0.0814 | 0.2764 | ||||||||
| BRem1 | 0.4200 | 0.4880 | 0.3666 | −0.1933* (0.1044) | −0.1470 (0.1243) | −0.2429** (0.1160) | 0.5478 | 0.5362 | 0.5288 | 0.0231 (0.0367) | 0.0154 (0.0782) | −0.0333 (0.0600) |
| BRem12 | −0.0156 | −0.0190 | −0.0134 | 0.0086* (0.0044) | 0.0069 (0.0054) | 0.0104** (0.0049) | −0.0221 | −0.0221 | −0.0213 | −0.0006 (0.0017) | −0.0002 (0.0033) | 0.0019 (0.0026) |
| Extrema point | 13.4537 | 12.8690 | 13.7106 | 11.2071 | 10.71618 | 11.7050 | 12.3997 | 12.1111 | 12.4305 | 18.38442 | 45.4338 | 8.5387 |
| Lind–Melhum | 2.410 | 3.610 | 2.080 | 1.640 | 1.030 | 2.030 | 9.430 | 6.630 | 8.460 | – | – | 0.240 |
| p-value | (0.009) | (0.000) | (0.020) | (0.052) | (0.152) | (0.023) | (0.000) | (0.000) | (0.000) | – | – | (0.407) |
| BOwn | −0.2762 | −0.2959 | −0.3200 | −0.2615 | −0.2341 | −0.2361 | −0.0271 (0.0213) | −0.1005 | −0.0966 | −0.1377 | −0.1908 | −0.2001 |
| BIndep | −0.2340 | −0.2120 | −0.2673 | −0.0920 (0.0662) | −0.2002 | −0.1326** (0.0513) | −0.1333 | −0.1436 | −0.1397 | −0.0187 (0.0369) | −0.0833 | −0.0732 |
| BSize | −0.0275 | −0.0318 | −0.0375 | −0.0415 | −0.0491 | −0.0484 | −0.0066** (0.0026) | −0.0112 | −0.0099 | −0.0075 (0.0047) | −0.0103** (0.0048) | −0.0097** (0.0049) |
| BExec | −0.4452 | −0.3479 | −0.5808 | −0.5735 | −0.6867 | −0.6732 | −0.2605 | −0.3304 | −0.2828 | 0.0729 (0.0661) | 0.1029 (0.0689) | 0.0473 (0.0721) |
| Size | 0.0258** (0.0129) | 0.0277** (0.0113) | 0.0289 | 0.0427 | 0.0564 | 0.0674 | 0.0240 | 0.0289 | 0.0211 | 0.0186** (0.0092) | 0.0131 (0.0092) | 0.0153* (0.0086) |
| Risk | 0.0018 (0.0071) | 0.0046 (0.0076) | 0.0147** (0.0065) | −0.0069 (0.0091) | 0.0097 (0.0066) | 0.0073 (0.0067) | 0.0259 | 0.0228 | 0.0246 | 0.0395 | 0.0465 | 0.0466 |
| ROA | 0.5212 | 0.2808* (0.1510) | 0.4281** (0.2005) | −1.4305 | −1.4987 | −1.7102 | 0.6586 | 0.5401 | 0.5358 | 1.1369 | 1.0189 | 1.0307 |
| Lev | −0.4119 | −0.4900 | −0.4276 | −0.4985 | −0.4249 | −0.5097 | 0.0316 (0.0463) | −0.0992** (0.0483) | −0.0032 (0.0651) | 0.1447** (0.0708) | 0.1958** (0.0765) | 0.2189 |
| GO | 0.0565 | 0.0531 | 0.0423 | −0.0095 (0.0142) | −0.0208 (0.0133) | 0.0024 (0.0161) | −0.0064 (0.0103) | 0.0105 (0.0086) | 0.0023 (0.0095) | −0.0024 (0.0104) | −0.0087 (0.0119) | −0.0045 (0.0107) |
| Governance1 | −0.0210 | −0.0191 | −0.0186 | −0.0172 | −0.0197 | −0.0165 | −0.0064 | −0.0036* (0.0019) | −0.0050** (0.0021) | −0.0202 | −0.0193 | −0.0209 |
| Governance2 | −0.0007 (0.0082) | 0.0123 (0.0132) | 0.0107 (0.0106) | 0.0148 (0.0152) | 0.0177 (0.0133) | 0.0205 (0.0135) | −0.0097* (0.0049) | −0.0014 (0.0062) | −0.0008 (0.0057) | −0.0223 | −0.0152** (0.0072) | −0.0133** (0.0062) |
| Governance3 | −0.0026 (0.0168) | 0.0128 (0.0169) | 0.0246 (0.0177) | −0.0257 (0.0190) | −0.0080 (0.0188) | 0.0030 (0.0208) | −0.0470 | −0.0349 | −0.0437 | 0.0202 (0.0139) | 0.0131 (0.0131) | 0.0125 (0.0122) |
| Constant | −2.3971 | −2.7375 | −2.0316 | 1.4667** (0.5731) | 0.9913 (0.6782) | 1.3639** (0.6316) | −3.2354 | −3.0967 | −3.0226 | −0.1898 (0.2442) | −0.0284 (0.3874) | 0.1953 (0.3407) |
| Observations | 486 | 486 | 486 | 490 | 490 | 490 | 473 | 473 | 473 | 484 | 484 | 484 |
| F | 201.360 | 130.340 | 312.720 | 30.450 | 39.970 | 35.640 | 359.720 | 2550.900 | 380.240 | 1877.500 | 520.600 | 869.280 |
| p-value | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) |
| AR(2) | 1.767 | 1.784 | 1.826 | 1.165 | 1.188 | 1.239 | 1.803 | 1.902 | 1.914 | 0.758 | 0.701 | 0.701 |
| p-value | (0.077) | (0.000) | (0.000) | (0.000) | (0.000) | (0.215) | (0.002) | (0.002) | (0.056) | (0.449) | (0.000) | (0.000) |
| Sargan | 118.000 | 119.300 | 122.500 | 121.600 | 127.700 | 131.700 | 155.200 | 157.100 | 159.200 | 155.100 | 152.900 | 152.500 |
Notes:
Earnings-quality regression estimates (EQ1, EQ2, EQ3 and EQ4).
The table shows the regression estimates with GMM system estimator with fixed effects at the firm and industry levels. EQ1, EQ2, EQ3 and EQ4 are used as the dependent variable. GD1 to GD3 are the alternative gender-diversity metrics for our first independent variable. BRem1 is the measure of remuneration of the board of directors, related to the hypotheses regarding the directors’ compensation. BOwn is the percentage of equity capital owned by the board of directors, BIndep is the percentage of independent board members, BSize is calculated as the number of directors on the board of directors and BExec is the percentage of executive directors. Size measures firm size as the logarithm of total assets. Risk measures the default risk based on Altman’s (1968) Z-score. ROA measure the return on assets, computed as net income over total assets. Lev measures the leverage ratio, calculated as the sum of short- and long-term debt over total assets. GO is a metric of growth opportunities, defined as total assets less total common equity plus the company’s market capitalization, all scaled by total assets. Finally, factor variables from the Cluster-Focused Principal Component Factoring technique (Governance1, Governance2 and Governance3) that capture key attributes of the Unified Good Governance Code were also included. The table also shows the extrema point (or threshold) for each regression using the BRem1 measure, which is tested with Lind and Mehlum’s (2010) contrast.
z-statistics in parentheses. Robust standard errors are reported.
***, ** and * simply significance at 1, 5 and 10%, respectively.
Extremum outside interval, which leads to trivial failure to reject null hypothesis of monotone or U-shaped relationship
Correlation matrix
| Variables | EQ1 | EQ2 | EQ3 | EQ4 | EM | GD1 | GD2 | GD3 | BRem1 | BRem2 | BRem3 | BRem4 | BOwn | BIndep | BSize | BExec | Size | Risk | ROA | Lev |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| EQ2 | 0.524*** | 1.000 | ||||||||||||||||||
| EQ3 | 0.751*** | 0.114* | 1.000 | |||||||||||||||||
| EQ4 | 0.752*** | 0.00687 | 0.531*** | 1.000 | ||||||||||||||||
| EM | 0.0134 | −0.0192 | 0.0706 | 0.0258 | 1.000 | |||||||||||||||
| GD1 | 0.0741 | 0.0872 | 0.0186 | 0.0691 | 0.106* | 1.000 | ||||||||||||||
| GD2 | 0.0907 | 0.0522 | 0.0526 | 0.0963 | 0.126* | 0.958*** | 1.000 | |||||||||||||
| GD3 | 0.102 | 0.0439 | 0.0746 | 0.106* | 0.140** | 0.922*** | 0.991*** | 1.000 | ||||||||||||
| Brem1 | 0.120* | −0.00895 | 0.142** | 0.152** | 0.0482 | 0.133* | 0.182*** | 0.198*** | 1.000 | |||||||||||
| Brem2 | 0.151** | −0.00125 | 0.146** | 0.199*** | 0.00293 | 0.104* | 0.139** | 0.150** | 0.917*** | 1.000 | ||||||||||
| Brem3 | 0.0719 | 0.0438 | 0.114* | 0.0203 | −0.0316 | −0.00937 | 0.0142 | 0.0221 | −0.0909 | −0.167** | 1.000 | |||||||||
| Brem4 | 0.0441 | 0.0325 | 0.113* | −0.0291 | 0.0190 | 0.0219 | 0.0434 | 0.0500 | −0.0518 | −0.224*** | 0.851*** | 1.000 | ||||||||
| BOwn | −0.0428 | −0.0392 | 0.0238 | −0.0308 | 0.0146 | −0.109* | −0.0828 | −0.0749 | −0.243*** | −0.215*** | 0.104* | 0.0822 | 1.000 | |||||||
| BIndep | 0.0998 | −0.0387 | 0.0484 | 0.151** | −0.0157 | 0.202*** | 0.221*** | 0.211*** | 0.222*** | 0.219*** | −0.0638 | −0.0557 | −0.298*** | 1.000 | ||||||
| BSize | 0.0461 | 0.0201 | 0.0896 | 0.0119 | 0.137** | 0.0533 | 0.124* | 0.163** | 0.499*** | 0.415*** | −0.107* | −0.0814 | −0.188*** | −0.0361 | 1.000 | |||||
| BExec | 0.0437 | −0.0791 | −0.0138 | 0.198*** | −0.0243 | −0.0948 | −0.120* | −0.137** | 0.112* | 0.131* | 0.0846 | 0.0743 | 0.201*** | −0.0701 | −0.255*** | 1.000 | ||||
| Size | 0.0202 | 0.0184 | 0.0586 | 0.00229 | 0.162** | 0.202*** | 0.235*** | 0.249*** | 0.773*** | 0.706*** | −0.160** | −0.166** | −0.355*** | 0.247*** | 0.687*** | −0.173*** | 1.000 | |||
| Risk | 0.424*** | −0.0953 | 0.478*** | 0.529*** | 0.0422 | −0.0517 | −0.00991 | 0.0161 | 0.0650 | 0.114* | 0.0894 | 0.0380 | 0.206*** | −0.0302 | −0.113* | 0.270*** | −0.211*** | 1.000 | ||
| ROA | 0.421*** | −0.237*** | 0.497*** | 0.615*** | 0.144** | 0.0547 | 0.0973 | 0.118* | 0.0804 | 0.0737 | 0.147** | 0.120* | 0.0995 | 0.0408 | −0.0788 | 0.187*** | −0.0539 | 0.607*** | 1.000 | |
| Lev | −0.309*** | −0.0595 | −0.286*** | −0.304*** | −0.112* | 0.0374 | 0.00712 | −0.0241 | 0.00753 | −0.0256 | −0.110* | −0.0929 | −0.203*** | −0.0222 | 0.147** | −0.224*** | 0.201*** | −0.569*** | −0.328*** | 1.000 |
| GO | 0.340*** | −0.155** | 0.416*** | 0.444*** | −0.0575 | −0.0461 | −0.0155 | −0.00504 | 0.0917 | 0.120* | −0.0143 | −0.0316 | 0.155** | 0.00540 | −0.122* | 0.149** | −0.167** | 0.769*** | 0.519*** | −0.200*** |
| Variables | EQ1 | EQ2 | EQ3 | EQ4 | EM | GD1 | GD2 | GD3 | BRem1 | BRem2 | BRem3 | BRem4 | BOwn | BIndep | BSize | BExec | Size | Risk | ROA | Lev |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| EQ2 | 0.524 | 1.000 | ||||||||||||||||||
| EQ3 | 0.751 | 0.114* | 1.000 | |||||||||||||||||
| EQ4 | 0.752 | 0.00687 | 0.531 | 1.000 | ||||||||||||||||
| EM | 0.0134 | −0.0192 | 0.0706 | 0.0258 | 1.000 | |||||||||||||||
| GD1 | 0.0741 | 0.0872 | 0.0186 | 0.0691 | 0.106* | 1.000 | ||||||||||||||
| GD2 | 0.0907 | 0.0522 | 0.0526 | 0.0963 | 0.126* | 0.958 | 1.000 | |||||||||||||
| GD3 | 0.102 | 0.0439 | 0.0746 | 0.106* | 0.140** | 0.922 | 0.991 | 1.000 | ||||||||||||
| Brem1 | 0.120* | −0.00895 | 0.142** | 0.152** | 0.0482 | 0.133* | 0.182 | 0.198 | 1.000 | |||||||||||
| Brem2 | 0.151** | −0.00125 | 0.146** | 0.199 | 0.00293 | 0.104* | 0.139** | 0.150** | 0.917 | 1.000 | ||||||||||
| Brem3 | 0.0719 | 0.0438 | 0.114* | 0.0203 | −0.0316 | −0.00937 | 0.0142 | 0.0221 | −0.0909 | −0.167** | 1.000 | |||||||||
| Brem4 | 0.0441 | 0.0325 | 0.113* | −0.0291 | 0.0190 | 0.0219 | 0.0434 | 0.0500 | −0.0518 | −0.224 | 0.851 | 1.000 | ||||||||
| BOwn | −0.0428 | −0.0392 | 0.0238 | −0.0308 | 0.0146 | −0.109* | −0.0828 | −0.0749 | −0.243 | −0.215 | 0.104* | 0.0822 | 1.000 | |||||||
| BIndep | 0.0998 | −0.0387 | 0.0484 | 0.151** | −0.0157 | 0.202 | 0.221 | 0.211 | 0.222 | 0.219 | −0.0638 | −0.0557 | −0.298 | 1.000 | ||||||
| BSize | 0.0461 | 0.0201 | 0.0896 | 0.0119 | 0.137** | 0.0533 | 0.124* | 0.163** | 0.499 | 0.415 | −0.107* | −0.0814 | −0.188 | −0.0361 | 1.000 | |||||
| BExec | 0.0437 | −0.0791 | −0.0138 | 0.198 | −0.0243 | −0.0948 | −0.120* | −0.137** | 0.112* | 0.131* | 0.0846 | 0.0743 | 0.201 | −0.0701 | −0.255 | 1.000 | ||||
| Size | 0.0202 | 0.0184 | 0.0586 | 0.00229 | 0.162** | 0.202 | 0.235 | 0.249 | 0.773 | 0.706 | −0.160** | −0.166** | −0.355 | 0.247 | 0.687 | −0.173 | 1.000 | |||
| Risk | 0.424 | −0.0953 | 0.478 | 0.529 | 0.0422 | −0.0517 | −0.00991 | 0.0161 | 0.0650 | 0.114* | 0.0894 | 0.0380 | 0.206 | −0.0302 | −0.113* | 0.270 | −0.211 | 1.000 | ||
| ROA | 0.421 | −0.237 | 0.497 | 0.615 | 0.144** | 0.0547 | 0.0973 | 0.118* | 0.0804 | 0.0737 | 0.147** | 0.120* | 0.0995 | 0.0408 | −0.0788 | 0.187 | −0.0539 | 0.607 | 1.000 | |
| Lev | −0.309 | −0.0595 | −0.286 | −0.304 | −0.112* | 0.0374 | 0.00712 | −0.0241 | 0.00753 | −0.0256 | −0.110* | −0.0929 | −0.203 | −0.0222 | 0.147** | −0.224 | 0.201 | −0.569 | −0.328 | 1.000 |
| GO | 0.340 | −0.155** | 0.416 | 0.444 | −0.0575 | −0.0461 | −0.0155 | −0.00504 | 0.0917 | 0.120* | −0.0143 | −0.0316 | 0.155** | 0.00540 | −0.122* | 0.149** | −0.167** | 0.769 | 0.519 | −0.200 |
Notes:
This table provides a pairwise correlation matrix of the variables used in the empirical analyses. EQ1 to EQ4 as well as EM are the alternative metrics of earnings quality, which are the dependent variables in the main study. These metrics correspond to the measures of earnings quality developed by StarMine, based on a percentile ranking from 0 to 1 of stocks based on sustainability of earnings, with 1 representing the highest rank. These metrics consist of the overall measure of earnings quality (EQ1), earnings quality corresponding to an accruals measure (EQ2), a cash flow measure (EQ3) and an operating-efficiency measure (EQ4). EM is the measure of earnings quality based on discretionary accruals used in the robustness analyses. By construction, EM takes negative values, with higher values indicating better earnings quality. GD1 to GD3 are the alternative metrics of boards’ gender diversity for our first independent variable. BRem1 to BRem4 are the measures of remuneration of the board of directors, related to the hypotheses regarding the directors’ compensation. BOwn is the percentage of the equity capital owned by the board of directors, BIndep is the percentage of independent board members, BSize is the number of directors on the board of directors and BExec is the percentage of executive directors. Size is firm size as the logarithm of total assets. Risk measures the default risk based on Altman’s (1968) Z-score. ROA measure the return on assets, computed as net income over total assets. Lev measures the leverage ratio, defined as the sum of short- and long-term debt over total assets. Finally, GO is a metric of growth opportunities, defined as total assets less total common equity plus the company’s market capitalization, all scaled by total assets. ***, ** and * simply significance at 1, 5 and 10%, respectively
Because our proxy measures of earnings quality have not been applied in other contexts, the seminal application of these novel measures (EQ1 through EQ4) is reinforced with a variable that is widely used to measure earnings management (EM) as described by Dechow et al. (1995). These findings are tabulated in Table A7; the results are consistent with our initial results. We observe that the three measures of gender diversity exhibit a positive and statistically significant influence on EM. The estimations associated with board remuneration generally show a nonlinear influence: higher compensation improves earnings quality, but above a certain compensation level, earnings quality deteriorates. These results validate our findings.
Robustness using GMM-SE
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variables | EM | EM | EM | EM | EM | EM | EM | EM | EM | EM | EM | EM |
| GD1 | 0.1783*** (0.0208) | 0.0755*** (0.0157) | 0.1545*** (0.0080) | 0.0573*** (0.0114) | ||||||||
| GD2 | 0.1809*** (0.0172) | 0.0440*** (0.0092) | 0.1841*** (0.0063) | 0.0771*** (0.0102) | ||||||||
| GD3 | 0.1299*** (0.0108) | 0.0388*** (0.0066) | 0.1294*** (0.0039) | 0.0561*** (0.0070) | ||||||||
| BRem1 | 0.5201*** (0.0204) | 0.5078*** (0.0204) | 0.5064*** (0.0193) | |||||||||
| BRem12 | −0.0228*** (0.0009) | −0.0225*** (0.0009) | −0.0224*** (0.0008) | |||||||||
| BRem2 | 0.0605*** (0.0115) | 0.0667*** (0.0130) | 0.0708*** (0.0128) | |||||||||
| BRem22 | −0.0032*** (0.0005) | −0.0033*** (0.0006) | −0.0035*** (0.0006) | |||||||||
| BRem3 | 0.0109*** (0.0035) | −0.0004 (0.0035) | −0.0058* (0.0033) | |||||||||
| BRem32 | 0.0098** (0.0041) | 0.0265*** (0.0032) | 0.0342*** (0.0030) | |||||||||
| BRem4 | 0.0356*** (0.0125) | 0.0141 (0.0136) | 0.0017 (0.0141) | |||||||||
| BRem42 | 0.6135*** (0.0731) | 0.8050*** (0.0846) | 0.9196*** (0.0860) | |||||||||
| Extrema point | 11.4030 | 11.2866 | 11.3164 | 9.4729 | 10.1752 | 10.0852 | −0.5536 | 0.0081 | 0.0848 | −0.0290 | −0.0088 | −0.0009 |
| Lind–Melhum | 22.360 | 23.090 | 24.000 | 4.430 | 4.800 | 5.180 | – | 4.380 | 6.830 | 4.350 | 5.760 | 6.980 |
| p-value | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | – | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) |
| BOwn | −0.0164*** (0.0062) | −0.0138 (0.0099) | −0.0307*** (0.0094) | −0.0443*** (0.0086) | −0.0407*** (0.0114) | −0.0609*** (0.0097) | −0.0792*** (0.0093) | −0.0913*** (0.0075) | −0.0999*** (0.0069) | −0.0096 (0.0108) | −0.0182 (0.0112) | −0.0275** (0.0114) |
| BIndep | −0.0693*** (0.0107) | −0.0504*** (0.0084) | −0.0583*** (0.0080) | −0.1466*** (0.0135) | −0.1241*** (0.0167) | −0.1592*** (0.0109) | −0.0401*** (0.0090) | −0.0560*** (0.0105) | −0.0758*** (0.0099) | −0.0080 (0.0083) | 0.0027 (0.0065) | −0.0065 (0.0060) |
| BSize | 0.0019** (0.0009) | 0.0011 (0.0016) | 0.0008 (0.0016) | −0.0002 (0.0013) | 0.0008 (0.0012) | 0.0015 (0.0013) | 0.0011 (0.0009) | −0.0002 (0.0008) | −0.0007 (0.0007) | 0.0077*** (0.0006) | 0.0064*** (0.0008) | 0.0061*** (0.0009) |
| BExec | −0.1196*** (0.0162) | −0.1094*** (0.0168) | −0.1112*** (0.0166) | −0.0764*** (0.0162) | −0.0922*** (0.0166) | −0.0738*** (0.0189) | −0.1134*** (0.0147) | −0.0787*** (0.0146) | −0.0681*** (0.0126) | −0.0774*** (0.0145) | −0.0513*** (0.0154) | −0.0481*** (0.0144) |
| Size | 0.0081*** (0.0014) | 0.0107*** (0.0021) | 0.0103*** (0.0020) | 0.0054** (0.0022) | 0.0017 (0.0021) | 0.0011 (0.0029) | −0.0051*** (0.0014) | −0.0057*** (0.0012) | −0.0055*** (0.0013) | 0.0217*** (0.0023) | 0.0202*** (0.0019) | 0.0213*** (0.0018) |
| Risk | −0.0186*** (0.0018) | −0.0190*** (0.0015) | −0.0212*** (0.0013) | −0.0046*** (0.0017) | −0.0044** (0.0019) | −0.0075*** (0.0021) | 0.0006 (0.0012) | −0.0002 (0.0015) | −0.0009 (0.0012) | −0.0049*** (0.0011) | −0.0038*** (0.0012) | −0.0039*** (0.0013) |
| ROA | 0.2179*** (0.0328) | 0.1929*** (0.0247) | 0.1842*** (0.0211) | 0.4592*** (0.0274) | 0.4395*** (0.0290) | 0.4956*** (0.0359) | 0.3333*** (0.0140) | 0.2432*** (0.0136) | 0.2505*** (0.0132) | 0.3175*** (0.0160) | 0.2317*** (0.0157) | 0.2402*** (0.0164) |
| Lev | −0.3086*** (0.0219) | −0.3199*** (0.0241) | −0.3387*** (0.0235) | −0.3011*** (0.0174) | −0.3334*** (0.0192) | −0.3539*** (0.0172) | −0.1452*** (0.0111) | −0.1807*** (0.0097) | −0.1882*** (0.0088) | −0.1585*** (0.0123) | −0.1914*** (0.0125) | −0.2018*** (0.0141) |
| GO | 0.0410*** (0.0027) | 0.0430*** (0.0021) | 0.0445*** (0.0020) | −0.0096** (0.0047) | −0.0152*** (0.0052) | −0.0147** (0.0057) | −0.0095*** (0.0024) | −0.0076*** (0.0026) | −0.0080*** (0.0022) | 0.0146*** (0.0018) | 0.0107*** (0.0020) | 0.0102*** (0.0019) |
| Governance1 | −0.0005 (0.0003) | 0.0006 (0.0004) | 0.0005 (0.0003) | 0.0013** (0.0006) | 0.0007 (0.0007) | 0.0014** (0.0007) | −0.0028*** (0.0004) | −0.0018*** (0.0004) | −0.0022*** (0.0004) | −0.0043*** (0.0005) | −0.0045*** (0.0004) | −0.0044*** (0.0005) |
| Governance2 | −0.0033*** (0.0010) | −0.0028** (0.0013) | −0.0028** (0.0012) | 0.0168*** (0.0020) | 0.0126*** (0.0021) | 0.0151*** (0.0014) | −0.0088*** (0.0012) | −0.0068*** (0.0014) | −0.0063*** (0.0014) | 0.0020* (0.0010) | 0.0017* (0.0010) | 0.0023** (0.0010) |
| Governance3 | 0.0123*** (0.0028) | 0.0165*** (0.0038) | 0.0190*** (0.0035) | −0.0144*** (0.0041) | −0.0066 (0.0054) | −0.0142*** (0.0046) | −0.0033 (0.0021) | −0.0035 (0.0022) | −0.0062*** (0.0021) | −0.0431*** (0.0027) | −0.0385*** (0.0031) | −0.0414*** (0.0035) |
| Constant | −3.0682*** (0.1103) | −3.0339*** (0.1122) | −3.0143*** (0.1094) | −0.2730*** (0.0660) | −0.2553*** (0.0733) | −0.2455*** (0.0911) | 0.1014*** (0.0271) | 0.1238*** (0.0255) | 0.1317*** (0.0287) | −0.5573*** (0.0470) | −0.5151*** (0.0398) | −0.5286*** (0.0370) |
| Observations | 440 | 440 | 440 | 434 | 434 | 434 | 440 | 440 | 440 | 448 | 448 | 448 |
| F | 7423.300 | 1048.780 | 15872.440 | 78918.600 | 5179.170 | 4413.790 | 24179.970 | 93901.800 | 23659.810 | 2869.210 | 3083.290 | 5617.310 |
| p-value | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) |
| AR(2) | −0.266 | −0.144 | −0.0322 | −0.163 | −0.115 | −0.144 | −0.0203 | 0.259 | 0.302 | −0.781 | −0.602 | −0.581 |
| p-value | 0.790 | 0.0164 | 0.0141 | 0.0486 | 0.908 | 0.0377 | 0.984 | 0.0511 | 0.0474 | 0.0180 | 0.0182 | 0.0173 |
| Sargan | 276.5 | 269.6 | 267.4 | 230 | 233.5 | 229.7 | 274.7 | 264.8 | 260.1 | 346.3 | 337.3 | 327.6 |
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variables | EM | EM | EM | EM | EM | EM | EM | EM | EM | EM | EM | EM |
| GD1 | 0.1783 | 0.0755 | 0.1545 | 0.0573 | ||||||||
| GD2 | 0.1809 | 0.0440 | 0.1841 | 0.0771 | ||||||||
| GD3 | 0.1299 | 0.0388 | 0.1294 | 0.0561 | ||||||||
| BRem1 | 0.5201 | 0.5078 | 0.5064 | |||||||||
| BRem12 | −0.0228 | −0.0225 | −0.0224 | |||||||||
| BRem2 | 0.0605 | 0.0667 | 0.0708 | |||||||||
| BRem22 | −0.0032 | −0.0033 | −0.0035 | |||||||||
| BRem3 | 0.0109 | −0.0004 (0.0035) | −0.0058* (0.0033) | |||||||||
| BRem32 | 0.0098** (0.0041) | 0.0265 | 0.0342 | |||||||||
| BRem4 | 0.0356 | 0.0141 (0.0136) | 0.0017 (0.0141) | |||||||||
| BRem42 | 0.6135 | 0.8050 | 0.9196 | |||||||||
| Extrema point | 11.4030 | 11.2866 | 11.3164 | 9.4729 | 10.1752 | 10.0852 | −0.5536 | 0.0081 | 0.0848 | −0.0290 | −0.0088 | −0.0009 |
| Lind–Melhum | 22.360 | 23.090 | 24.000 | 4.430 | 4.800 | 5.180 | – | 4.380 | 6.830 | 4.350 | 5.760 | 6.980 |
| p-value | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | – | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) |
| BOwn | −0.0164 | −0.0138 (0.0099) | −0.0307 | −0.0443 | −0.0407 | −0.0609 | −0.0792 | −0.0913 | −0.0999 | −0.0096 (0.0108) | −0.0182 (0.0112) | −0.0275** (0.0114) |
| BIndep | −0.0693 | −0.0504 | −0.0583 | −0.1466 | −0.1241 | −0.1592 | −0.0401 | −0.0560 | −0.0758 | −0.0080 (0.0083) | 0.0027 (0.0065) | −0.0065 (0.0060) |
| BSize | 0.0019** (0.0009) | 0.0011 (0.0016) | 0.0008 (0.0016) | −0.0002 (0.0013) | 0.0008 (0.0012) | 0.0015 (0.0013) | 0.0011 (0.0009) | −0.0002 (0.0008) | −0.0007 (0.0007) | 0.0077 | 0.0064 | 0.0061 |
| BExec | −0.1196 | −0.1094 | −0.1112 | −0.0764 | −0.0922 | −0.0738 | −0.1134 | −0.0787 | −0.0681 | −0.0774 | −0.0513 | −0.0481 |
| Size | 0.0081 | 0.0107 | 0.0103 | 0.0054** (0.0022) | 0.0017 (0.0021) | 0.0011 (0.0029) | −0.0051 | −0.0057 | −0.0055 | 0.0217 | 0.0202 | 0.0213 |
| Risk | −0.0186 | −0.0190 | −0.0212 | −0.0046 | −0.0044** (0.0019) | −0.0075 | 0.0006 (0.0012) | −0.0002 (0.0015) | −0.0009 (0.0012) | −0.0049 | −0.0038 | −0.0039 |
| ROA | 0.2179 | 0.1929 | 0.1842 | 0.4592 | 0.4395 | 0.4956 | 0.3333 | 0.2432 | 0.2505 | 0.3175 | 0.2317 | 0.2402 |
| Lev | −0.3086 | −0.3199 | −0.3387 | −0.3011 | −0.3334 | −0.3539 | −0.1452 | −0.1807 | −0.1882 | −0.1585 | −0.1914 | −0.2018 |
| GO | 0.0410 | 0.0430 | 0.0445 | −0.0096** (0.0047) | −0.0152 | −0.0147** (0.0057) | −0.0095 | −0.0076 | −0.0080 | 0.0146 | 0.0107 | 0.0102 |
| Governance1 | −0.0005 (0.0003) | 0.0006 (0.0004) | 0.0005 (0.0003) | 0.0013** (0.0006) | 0.0007 (0.0007) | 0.0014** (0.0007) | −0.0028 | −0.0018 | −0.0022 | −0.0043 | −0.0045 | −0.0044 |
| Governance2 | −0.0033 | −0.0028** (0.0013) | −0.0028** (0.0012) | 0.0168 | 0.0126 | 0.0151 | −0.0088 | −0.0068 | −0.0063 | 0.0020* (0.0010) | 0.0017* (0.0010) | 0.0023** (0.0010) |
| Governance3 | 0.0123 | 0.0165 | 0.0190 | −0.0144 | −0.0066 (0.0054) | −0.0142 | −0.0033 (0.0021) | −0.0035 (0.0022) | −0.0062 | −0.0431 | −0.0385 | −0.0414 |
| Constant | −3.0682 | −3.0339 | −3.0143 | −0.2730 | −0.2553 | −0.2455 | 0.1014 | 0.1238 | 0.1317 | −0.5573 | −0.5151 | −0.5286 |
| Observations | 440 | 440 | 440 | 434 | 434 | 434 | 440 | 440 | 440 | 448 | 448 | 448 |
| F | 7423.300 | 1048.780 | 15872.440 | 78918.600 | 5179.170 | 4413.790 | 24179.970 | 93901.800 | 23659.810 | 2869.210 | 3083.290 | 5617.310 |
| p-value | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) |
| AR(2) | −0.266 | −0.144 | −0.0322 | −0.163 | −0.115 | −0.144 | −0.0203 | 0.259 | 0.302 | −0.781 | −0.602 | −0.581 |
| p-value | 0.790 | 0.0164 | 0.0141 | 0.0486 | 0.908 | 0.0377 | 0.984 | 0.0511 | 0.0474 | 0.0180 | 0.0182 | 0.0173 |
| Sargan | 276.5 | 269.6 | 267.4 | 230 | 233.5 | 229.7 | 274.7 | 264.8 | 260.1 | 346.3 | 337.3 | 327.6 |
Notes:
Earnings quality regression estimates (EM).
The table shows the regression estimates with GMM system estimator with fixed effects at the firm and industry levels. EQ1, EQ2, EQ3 and EQ4 are used as the dependent variable. GD1 to GD3 are the alternative gender-diversity metrics for our first independent variable. BRem1 to BRem4 are the alternative measures of remuneration of the board of directors, related to the hypotheses regarding the directors’ compensation. BOwn is the percentage of equity capital owned by the board of directors, BIndep is the percentage of independent board members, BSize is the number of directors on the board of directors and BExec is the percentage of executive directors. Size measures firm size as the logarithm of total assets. Risk measures the default risk based on Altman’s (1968) Z-score. ROA measures the return on assets, computed as net income over total assets. Lev measures the leverage ratio, defined as the sum of short- and long-term debt over total assets. GO is a metric of growth opportunities, defined as total assets less total common equity plus the company’s market capitalization, all scaled by total assets. Finally, factor variables from the Cluster-Focused Principal Component Factoring technique (Governance1, Governance2 and Governance3) that capture key attributes of the Unified Good Governance Code were also included. The table also shows the extrema point (or threshold) for each regression using the BRem1 measure, which is tested with Lind and Mehlum’s (2010) contrast.
z-statistics in parentheses.
Robust standard errors are reported.
***, ** and * simply significance at 1, 5 and 10%, respectively.
Extremum outside interval, which leads to trivial failure to reject null hypothesis of monotone or U-shaped relationship
Finally, as a last check for endogeneity issues, we apply the GMM in first differencing (GMM-Dif) according to Arellano and Bond (1991). Table A8 – which replicates the outputs obtained in Table A6 – shows that regardless of the metric for board gender diversity, the statistical significance and sign of the estimated parameters is consistent with the previous findings. Similarly, the board-remuneration variable (BRem1) still displays a nonmonotonic relationship with earnings quality. We conclude that while we lack a valid instrumental variable to deal with endogeneity issues for the covariates that are not strictly exogenous, GMM is an efficient technique to deal with this econometric problem (Antonakis et al., 2021; Barros et al., 2019).
Robustness using GMM-Dif
| Variables | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| EQ1 | EQ1 | EQ1 | EQ2 | EQ2 | EQ2 | EQ3 | EQ3 | EQ3 | EQ4 | EQ4 | EQ4 | |
| DepVart−1 | −0.0924*** (0.0322) | −0.0795*** (0.0281) | −0.0934*** (0.0295) | −0.0624*** (0.0147) | −0.0714*** (0.0164) | −0.0721*** (0.0194) | 0.1132*** (0.0128) | 0.1251*** (0.0133) | 0.1258*** (0.0118) | −0.0334 (0.0319) | −0.0325 (0.0330) | −0.0247 (0.0326) |
| GD1 | 0.8893*** (0.0640) | 0.6904*** (0.0574) | 0.1443*** (0.0241) | 0.6019*** (0.0578) | ||||||||
| GD2 | 0.6161*** (0.0563) | 0.4610*** (0.0417) | 0.1005*** (0.0156) | 0.3650*** (0.0323) | ||||||||
| GD3 | 0.3843*** (0.0366) | 0.2767*** (0.0291) | 0.0705*** (0.0107) | 0.2331*** (0.0218) | ||||||||
| BRem1 | 0.8291*** (0.0367) | 0.4466*** (0.1489) | 0.6143*** (0.1195) | 0.3301*** (0.0858) | 0.2890*** (0.0933) | 0.2624*** (0.0936) | 0.5950*** (0.0311) | 0.6363*** (0.0332) | 0.6277*** (0.0303) | 0.2182*** (0.0418) | 0.1973*** (0.0360) | 0.1753* (0.0944) |
| BRem12 | −0.0327*** (0.0017) | −0.0172*** (0.0061) | −0.0242*** (0.0049) | −0.0113*** (0.0039) | −0.0101** (0.0042) | −0.0089** (0.0043) | −0.0245*** (0.0013) | −0.0261*** (0.0014) | −0.0260*** (0.0013) | −0.0074*** (0.0019) | −0.0066*** (0.0016) | −0.0058 (0.0039) |
| Extrema point | 12.664 | 12.96904 | 12.69413 | 14.58733 | 14.32831 | 14.77827 | 12.15963 | 12.18558 | 12.08468 | 14.78963 | 14.88313 | 15.1503 |
| Lind–Melhum | 12.100 | 2.260 | 4.160 | 1.040 | 14.328 | 0.660 | 17.280 | 18.550 | 17.780 | 1.290 | 1.290 | 0.510 |
| p-value | (0.000) | (0.012) | (0.000) | (0.151) | (0.169) | (0.254) | (0.000) | (0.000) | (0.000) | (0.098) | (0.099) | (0.307) |
| BOwn | −0.3589*** (0.0369) | −0.3180*** (0.0411) | −0.3379*** (0.0443) | −0.3442*** (0.0440) | −0.3399*** (0.0367) | −0.3312*** (0.0481) | −0.1388*** (0.0160) | −0.1309*** (0.0138) | −0.1420*** (0.0146) | −0.2121*** (0.0382) | −0.1893*** (0.0364) | −0.1906*** (0.0353) |
| BIndep | 0.0045 (0.0395) | 0.1040*** (0.0402) | 0.1279*** (0.0398) | 0.0119 (0.0760) | 0.0712 (0.0683) | 0.0051 (0.0778) | −0.0707** (0.0283) | −0.0430 (0.0270) | −0.0546** (0.0233) | 0.0009 (0.0368) | 0.0451 (0.0382) | 0.0655*** (0.0243) |
| BSize | −0.0203*** (0.0044) | −0.0283*** (0.0051) | −0.0257*** (0.0044) | −0.0113** (0.0050) | −0.0157*** (0.0042) | −0.0161*** (0.0042) | −0.0113*** (0.0011) | −0.0103*** (0.0021) | −0.0119*** (0.0015) | −0.0020 (0.0045) | −0.0019 (0.0054) | −0.0002 (0.0052) |
| BExec | −0.1257 (0.0809) | −0.1592** (0.0698) | −0.0920 (0.0774) | −0.1871** (0.0819) | −0.2120** (0.0828) | −0.2266*** (0.0772) | 0.0026 (0.0352) | 0.0624* (0.0359) | 0.0566 (0.0423) | 0.0923* (0.0477) | 0.2878** (0.1131) | 0.2122** (0.1062) |
| Size | −0.2406*** (0.0177) | −0.1700*** (0.0302) | −0.1980*** (0.0304) | −0.0498*** (0.0164) | −0.0210 (0.0202) | −0.0100 (0.0214) | −0.0511*** (0.0146) | −0.0586*** (0.0130) | −0.0460*** (0.0137) | −0.1230*** (0.0178) | −0.0909*** (0.0214) | −0.0847*** (0.0220) |
| Risk | 0.0340*** (0.0069) | 0.0292*** (0.0059) | 0.0243*** (0.0067) | −0.0066 (0.0058) | −0.0109 (0.0074) | −0.0116 (0.0074) | 0.0168*** (0.0021) | 0.0154*** (0.0021) | 0.0149*** (0.0020) | 0.0583*** (0.0069) | 0.0541*** (0.0069) | 0.0598*** (0.0076) |
| ROA | 0.5820*** (0.1180) | 0.1752 (0.1462) | 0.3619** (0.1417) | −1.2242*** (0.1001) | −1.3311*** (0.1085) | −1.3960*** (0.1264) | 0.8521*** (0.0864) | 0.8643*** (0.0891) | 0.8028*** (0.0728) | 1.0041*** (0.1114) | 0.9632*** (0.1191) | 0.9017*** (0.1275) |
| Lev | −0.4273*** (0.1131) | −0.5834*** (0.1130) | −0.5686*** (0.1165) | −1.0921*** (0.1197) | −1.0964*** (0.1189) | −1.0037*** (0.1433) | 0.1952*** (0.0416) | 0.1478*** (0.0561) | 0.1739*** (0.0458) | 0.1205 (0.0743) | 0.0717 (0.0684) | 0.0776 (0.0705) |
| GO | −0.0419*** (0.0133) | −0.0344*** (0.0123) | −0.0364*** (0.0120) | −0.0594*** (0.0102) | −0.0507*** (0.0094) | −0.0421*** (0.0079) | −0.0237*** (0.0045) | −0.0231*** (0.0039) | −0.0204*** (0.0040) | −0.0643*** (0.0111) | −0.0653*** (0.0100) | −0.0714*** (0.0115) |
| Governance1 | −0.0197*** (0.0038) | −0.0221*** (0.0042) | −0.0229*** (0.0039) | −0.0192*** (0.0035) | −0.0215*** (0.0035) | −0.0201*** (0.0036) | −0.0010 (0.0015) | −0.0015 (0.0013) | −0.0003 (0.0015) | −0.0205*** (0.0029) | −0.0233*** (0.0030) | −0.0230*** (0.0026) |
| Governance2 | −0.0207*** (0.0033) | −0.0251*** (0.0044) | −0.0227*** (0.0044) | −0.0160*** (0.0054) | −0.0139** (0.0068) | −0.0127* (0.0069) | −0.0033** (0.0015) | −0.0026 (0.0017) | −0.0013 (0.0018) | −0.0193*** (0.0036) | −0.0185*** (0.0037) | −0.0186*** (0.0042) |
| Governance3 | 0.0195 (0.0129) | 0.0107 (0.0143) | 0.0160 (0.0145) | 0.0007 (0.0120) | −0.0161 (0.0124) | −0.0240 (0.0154) | −0.0022 (0.0072) | 0.0010 (0.0055) | −0.0015 (0.0059) | −0.0019 (0.0067) | −0.0064 (0.0080) | −0.0019 (0.0064) |
| Constant | 0.5681** (0.2679) | 1.4912* (0.7627) | 1.0533 (0.7242) | −0.2382 (0.4264) | −0.4841 (0.4673) | −0.5525 (0.4616) | −1.9490*** (0.3714) | −2.0790*** (0.3655) | −2.2381*** (0.3978) | 1.4062*** (0.3974) | 0.8516* (0.4932) | 0.8393 (0.6235) |
| Observations | 277 | 277 | 277 | 279 | 279 | 279 | 261 | 261 | 261 | 275 | 275 | 275 |
| Wald | 19,983 | 10,083 | 5,797 | 5,375 | 22,025 | 10,730 | 24,921 | 23,304 | 8,123 | 6,873 | 3,935 | 6,552 |
| p-value | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) |
| Variables | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| EQ1 | EQ1 | EQ1 | EQ2 | EQ2 | EQ2 | EQ3 | EQ3 | EQ3 | EQ4 | EQ4 | EQ4 | |
| DepVart−1 | −0.0924 | −0.0795 | −0.0934 | −0.0624 | −0.0714 | −0.0721 | 0.1132 | 0.1251 | 0.1258 | −0.0334 (0.0319) | −0.0325 (0.0330) | −0.0247 (0.0326) |
| GD1 | 0.8893 | 0.6904 | 0.1443 | 0.6019 | ||||||||
| GD2 | 0.6161 | 0.4610 | 0.1005 | 0.3650 | ||||||||
| GD3 | 0.3843 | 0.2767 | 0.0705 | 0.2331 | ||||||||
| BRem1 | 0.8291 | 0.4466 | 0.6143 | 0.3301 | 0.2890 | 0.2624 | 0.5950 | 0.6363 | 0.6277 | 0.2182 | 0.1973 | 0.1753* (0.0944) |
| BRem12 | −0.0327 | −0.0172 | −0.0242 | −0.0113 | −0.0101** (0.0042) | −0.0089** (0.0043) | −0.0245 | −0.0261 | −0.0260 | −0.0074 | −0.0066 | −0.0058 (0.0039) |
| Extrema point | 12.664 | 12.96904 | 12.69413 | 14.58733 | 14.32831 | 14.77827 | 12.15963 | 12.18558 | 12.08468 | 14.78963 | 14.88313 | 15.1503 |
| Lind–Melhum | 12.100 | 2.260 | 4.160 | 1.040 | 14.328 | 0.660 | 17.280 | 18.550 | 17.780 | 1.290 | 1.290 | 0.510 |
| p-value | (0.000) | (0.012) | (0.000) | (0.151) | (0.169) | (0.254) | (0.000) | (0.000) | (0.000) | (0.098) | (0.099) | (0.307) |
| BOwn | −0.3589 | −0.3180 | −0.3379 | −0.3442 | −0.3399 | −0.3312 | −0.1388 | −0.1309 | −0.1420 | −0.2121 | −0.1893 | −0.1906 |
| BIndep | 0.0045 (0.0395) | 0.1040 | 0.1279 | 0.0119 (0.0760) | 0.0712 (0.0683) | 0.0051 (0.0778) | −0.0707** (0.0283) | −0.0430 (0.0270) | −0.0546** (0.0233) | 0.0009 (0.0368) | 0.0451 (0.0382) | 0.0655 |
| BSize | −0.0203 | −0.0283 | −0.0257 | −0.0113** (0.0050) | −0.0157 | −0.0161 | −0.0113 | −0.0103 | −0.0119 | −0.0020 (0.0045) | −0.0019 (0.0054) | −0.0002 (0.0052) |
| BExec | −0.1257 (0.0809) | −0.1592** (0.0698) | −0.0920 (0.0774) | −0.1871** (0.0819) | −0.2120** (0.0828) | −0.2266 | 0.0026 (0.0352) | 0.0624* (0.0359) | 0.0566 (0.0423) | 0.0923* (0.0477) | 0.2878** (0.1131) | 0.2122** (0.1062) |
| Size | −0.2406 | −0.1700 | −0.1980 | −0.0498 | −0.0210 (0.0202) | −0.0100 (0.0214) | −0.0511 | −0.0586 | −0.0460 | −0.1230 | −0.0909 | −0.0847 |
| Risk | 0.0340 | 0.0292 | 0.0243 | −0.0066 (0.0058) | −0.0109 (0.0074) | −0.0116 (0.0074) | 0.0168 | 0.0154 | 0.0149 | 0.0583 | 0.0541 | 0.0598 |
| ROA | 0.5820 | 0.1752 (0.1462) | 0.3619** (0.1417) | −1.2242 | −1.3311 | −1.3960 | 0.8521 | 0.8643 | 0.8028 | 1.0041 | 0.9632 | 0.9017 |
| Lev | −0.4273 | −0.5834 | −0.5686 | −1.0921 | −1.0964 | −1.0037 | 0.1952 | 0.1478 | 0.1739 | 0.1205 (0.0743) | 0.0717 (0.0684) | 0.0776 (0.0705) |
| GO | −0.0419 | −0.0344 | −0.0364 | −0.0594 | −0.0507 | −0.0421 | −0.0237 | −0.0231 | −0.0204 | −0.0643 | −0.0653 | −0.0714 |
| Governance1 | −0.0197 | −0.0221 | −0.0229 | −0.0192 | −0.0215 | −0.0201 | −0.0010 (0.0015) | −0.0015 (0.0013) | −0.0003 (0.0015) | −0.0205 | −0.0233 | −0.0230 |
| Governance2 | −0.0207 | −0.0251 | −0.0227 | −0.0160 | −0.0139** (0.0068) | −0.0127* (0.0069) | −0.0033** (0.0015) | −0.0026 (0.0017) | −0.0013 (0.0018) | −0.0193 | −0.0185 | −0.0186 |
| Governance3 | 0.0195 (0.0129) | 0.0107 (0.0143) | 0.0160 (0.0145) | 0.0007 (0.0120) | −0.0161 (0.0124) | −0.0240 (0.0154) | −0.0022 (0.0072) | 0.0010 (0.0055) | −0.0015 (0.0059) | −0.0019 (0.0067) | −0.0064 (0.0080) | −0.0019 (0.0064) |
| Constant | 0.5681** (0.2679) | 1.4912* (0.7627) | 1.0533 (0.7242) | −0.2382 (0.4264) | −0.4841 (0.4673) | −0.5525 (0.4616) | −1.9490 | −2.0790 | −2.2381 | 1.4062 | 0.8516* (0.4932) | 0.8393 (0.6235) |
| Observations | 277 | 277 | 277 | 279 | 279 | 279 | 261 | 261 | 261 | 275 | 275 | 275 |
| Wald | 19,983 | 10,083 | 5,797 | 5,375 | 22,025 | 10,730 | 24,921 | 23,304 | 8,123 | 6,873 | 3,935 | 6,552 |
| p-value | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) |
Notes:
Earnings-quality regression estimates (EQ1, EQ2, EQ3 and EQ4)
The table shows the regression estimates with GMM in difference with fixed effects at the firm and industry levels. EQ1, EQ2, EQ3 and EQ4 are used as the dependent variable. GD1 to GD3 are the alternative gender-diversity metrics for our first independent variable. BRem1 is the measure of remuneration of the board of directors, related to the hypotheses regarding the directors’ compensation. BOwn is the percentage of equity capital owned by the board of directors, BIndep is the percentage of independent board members, BSize is the number of directors on the board of directors and BExec is the percentage of executive directors. Size measures firm size as the logarithm of total assets. Risk measures the default risk based on Altman’s (1968) Z-score. ROA measures the return on assets, computed as net income over total assets. Lev measures the leverage ratio, defined as the sum of short- and long-term debt over total assets. GO is a metric of growth opportunities, defined as total assets less total common equity plus the company’s market capitalization, all scaled by total assets. Finally, factor variables from the Cluster-Focused Principal Component Factoring technique (Governance1, Governance2 and Governance3) that capture key attributes of the Unified Good Governance Code were also included. The table also shows the extrema point (or threshold) for each regression using the BRem1 measure, which is tested with Lind and Mehlum’s (2010) contrast.
z-statistics in parentheses. Robust standard errors are reported.
***, ** and * simply significance at 1, 5 and 10%, respectively.
Extremum outside interval, which leads to trivial failure to reject null hypothesis of monotone or U-shaped relationship
5. Conclusions
Corporate governance literature studies, among other topics, the opportunistic behavior of managers when they manipulate financial statements. Such malpractice erodes the quality of earnings. This paper investigates how board gender diversity and board remuneration influence earnings quality of nonfinancial Spanish listed firms. Our results exhibit a consistent and statistically significant positive impact of board gender diversity on earnings quality, indicating that gender-balanced boards are associated with more transparent financial reports and more informative earnings. We also find a concave relationship between board compensation and earnings quality, indicating that excessive compensation leads to more opportunistic manipulation of financial reporting which subsequently dilutes earnings quality.
Our study supports previous findings that board gender diversity can act as a corporate governance mechanism, as we found that gender-balanced boards are directly associated with earnings quality and financial transparency. Additionally, this study builds upon the scarce literature on Spain by exploring how board remuneration and various metrics of board gender diversity explain new measures of earnings quality.
Second, the results show that the linkage between board compensation and earnings quality is nonlinear. This novel finding can open a dialogue about the role of board compensation on the informativeness of financial reports. The nonmonotonic relationship also indicates that at some companies, boards scheme with managers and behave indulgently as remuneration increases and monitoring decreases. This lowers the quality of earnings, thus misrepresenting a company’s true financial performance. Our study shows that excessive board member compensation reduces earnings quality and that boards are lax at monitoring managers when their salaries are above the optimal amount.
Third, we applied a suitable econometric technique. We used a Tobit semiparametric estimator with fixed effects that accounts for the censored nature of the alternative proxies of the dependent variable (earnings-quality metrics developed by StarMine). This technique was then applied to a unique data set. No previous study in corporate governance used panel data with fixed effects to explain a censored dependent variable. Furthermore, we found little sensitivity in our findings when substituting a more conventional measure of earnings quality for our preferred measure. These results are robust to GMM-based applications, suggesting our findings are robust to multiple specifications.
Fourth, the case of Spain matters because Spain has developed corporate governance regulations. Some of these regulations were passed with a gradual implementation window. Our results show that this new legal framework will likely lead to increased transparency and efficiency in the Spanish capital market. For instance, Spain has improved its regulation related to gender-balanced board participation. Despite the absence of legal sanctions, its regulation has mitigated poor corporate governance. The small resulting advances in female participation have undeniably improved the transparency, efficiency and quality of accounting information.
Our study is not free of limitations. First, it only covers nonfinancial Spanish listed firms. Accordingly, our results cannot be generalized to all Spanish companies. Second, we did not study board remuneration by gender. Future research should shed light on the consequences of the board-remuneration gender gap. Third, although our study controls for some board features, it is silent about other characteristics that might influence the quality of financial reporting such as the existence of committees, the number of meetings, the interlocking of board members and other measures of board diversity such as seniority, education, family ties or race. This leaves open avenues for future research.
Notes
G20/OECD Principles of Corporate Governance. www.oecd.org/daf/ca/principles-corporate-governance.htm
Department of Economic and Social Affairs of the United Nations. https://sdgs.un.org/goals/goal5
Press release, 15 January 2020, available at www.cnmv.es/portal/verDoc.axd?t=%7B8381aade-130b-413d-8e39-c157765d3abf%7D
Gender equality policies in Spain. Update. www.europarl.europa.eu/RegData/etudes/STUD/2016/583112/IPOL_STU(2016)583112_EN.pdf
Table A1 in Appendix 3 describes the panel composition by the Global Industry Classification Standard.
The existing literature applies firm random effects in most cases when dealing with censored dependent variables. Random effects, however, are not plausible in the case of corporate governance because unobservable heterogeneity is more likely to remain as a time-invariant effect in each corporation individually, thus justifying the use of fixed effects.
StarMine (www.refinitiv.com/en/products/starmine-financial-modeling) has developed these measures to better evaluate firms’ actual earnings compared to their reported earnings.
Although GD2 and GD3 are comparably better measures of gender diversity than GD1, given that in the sample composition only a few firms have a proportion of female board members (GD1) beyond 50%, by construction, GD2 and GD3 would measure variations in female minority members rather than male minority members – allowing for a direct comparison to GD1. This is supported empirically by the high correlation among the three gender diversity variables exhibited in Appendix 3 Table A3.
Total remuneration includes salary, fixed remuneration, attendance fees, short-term variable remuneration, long-term variable remuneration, remuneration for membership of board committees, severance payments, variable remuneration in shares, long-term savings systems and other remuneration benefits and other items.
Remuneration categories i, ii, vi, vii and ix of previous footnote.
Table A2 of Appendix 3 provides summary statistics for both the independent and control variables used in this analysis.
As shown in Table A1 of Appendix 3.
A Z-score below 1.81 indicates a company is likely headed for bankruptcy. Companies scoring above 2.99 are not likely to go bankrupt. The higher the score, the lower the likelihood of bankruptcy risk.
Table A3 of Appendix 3 shows a correlation matrix of the variables used in this study.
The extrema point (or threshold) is computed by optimizing the estimated model as a function of the corresponding remuneration variable. For instance, using Model (1) in Table 1, the first derivative of EQ1 = 0.5669 × GD1 + 0.8390 × BRem1 × 0.0322 × BRem12 + Other variables with respect to BRem1 is calculated and set equal to zero: ∂EQ1/∂BRem1 = 0.8390 × 2 × 0.0322 × BRem1 = 0. Solving for BRem1 the extrema point equals 13.0370, and as this is log transformed, the exponentiating arrives to €459,089.29 in annual remuneration per director.
For further characteristics of Lind and Mehlum (2010) contrasts, refer to Appendix 1.
This corresponds to a 10.7% of the sample companies that paid a total board compensation (share of a company’s net income (BRem3) above the average extrema point of 13.10%. Although this extrema point may appear disproportionately high retribution for managers as a share of the net income, recall that this is a relative measure of the board remuneration, and that a significant proportion of it is fixed (67.1%). It is plausible to have such high extrema point because when the net income is extraordinarily small, the total compensation of the board (which is mostly fixed), would represent a sizable share of the net income.
Barros et al. (2019) and Antonakis et al. (2021) recognize GMM-based models to be superior in mitigating endogeneity issues in the corporate finance literature when instrumental variables and quasi-experiments are not feasible.
In our case, a quasi- or natural experiment corresponds to any type of externally determined shock that creates random variation in the metrics of gender diversity, board remuneration and the other board features. Such shocks are typically considered in the corporate finance literature to include board diversity policies (such as board gender quotas) and changes in regulation regarding compensation packages of board members. In our case, as Spain has not been subject of any of these policy changes during the period of analysis, the quasi-experiment approach is unfeasible.
The Stata command used in the Tobit semi-parametric regressions is pantob while the GMM-SE and GMM-Dif outputs use xtabond2 and xtabond, respectively.
Professor Pablo San Martin would like to thank the Research Department of the Universidad Catolica de la Santisima Concepcion for its partial funding of this study through the research grant DINREG 08/2022.
The authors wish to thank Farida Rehab, Nishika Khubchandani, Melissa Haskin and Harry David for their invaluable research assistance. Authors are also thankful for the comments and suggestions of Kirk Tennant, Pablo Swedberg, Xavier Cottini and Bartolomé Pascual Fuster. The authors thank commentary from seminar participants at the INEKA Conference (2019) celebrated by Verona University, Verona, Italy; and the 16th Finance, Risk and Accounting Perspectives Conference (2019), celebrated by Helka University, Helsinki, Finland. Professors Paolo Saona and Laura Muro thank the Scholarship Opportunity Fund of Saint Louis University for the financial support, and Professor Paolo Saona thanks the Spanish Ministry of Science and Innovation for its financial support (PID2020-114797GB-I00), and Universidad Pontificia Comillas for its financial support with the internal research grant PP2022_11. All remaining errors remain our own.
References
Further reading
Appendix 1. Elaboration on the econometric techniques used in the empirical analyses
Since pooled Tobit models only fit random effects, and because there is no available strategy for a parametric conditional fixed-effects model, we follow Honoré’s (1992) method of a semiparametric estimator for fixed-effects Tobit models to report the primary marginal effects of our right-hand-side variables on our measures of earnings quality.
Adams (2016) suggests that instrumental variables or quasi-experiments should be applied to deal with the endogeneity issue related to board gender diversity [20]. However, in our case, and as recognized by Adams (2016) and Antonakis et al. (2021), the greatest challenge of such techniques is to find a valid instrument or set of instruments sufficiently correlated with the endogenous variables (e.g. our metrics of board gender diversity, board remuneration and board features). This difficulty is worsened by the unverifiable nature of the assumption that the instruments and the endogenous regressors are uncorrelated with the error term of the model (Antonakis et al., 2021). Consequently, our approach is rooted in the use of panel-data estimations, which allows us to mitigate endogeneity problems related to the regressors even in the absence of instruments that are exogenous to the model and in the absence of quasi- or natural-experiment contexts, this absence being common in most empirical studies in corporate finance (Barros et al., 2019). Hence, among the various approaches available for short-term panels and among those that use sequentially exogenous variables as instruments, we follow those based on the Generalized Method of Moments (GMM) for static models (Bond, 2002). To test the robustness of our findings, we use the procedure developed by Arellano and Bond (1991) known as first differencing (GMM-Dif) and the extension of this method developed by Blundell and Bond (1998) known as the system estimator (GMM-SE). The GMM-Dif procedure uses the first transformation of the variables of the model with the aim of eliminating unobserved heterogeneity in addition to a specific type of orthogonality (or noncorrelation) condition called a moment condition. These orthogonality conditions mean that the estimator will use all of the suitable lags of the right-hand-side variables as instrumental variables – that is, the variables assumed to be uncorrelated with the error term of the model (Arellano and Bond, 1991). The GMM-SE corresponds to an extended and enhanced version of the GMM-Dif because it uses the same moment conditions described above and adds others, which increases the efficiency and performance of the estimator in a finite sample (Blundell and Bond, 1998).
Barros et al. (2019) conduct a detailed examination of the estimators based on the general model of the simulation comparing the traditional ordinary least squares (OLS) estimator with the random-effect and fixed-effect estimators, as well as the GMM-based models mentioned above; they conclude that the GMM-Dif and particularly the GMM-SE estimators appropriately address all sources of endogeneity by removing the unobserved heterogeneity and using lags of the independent variables that are noncorrelated with the error term as instrumental variables. In their comparison, Barros et al. state that the estimated parameters of the GMM-based models are much closer to their true values when compared with the other panel methods. Similar conclusions are reached by Kiviet et al. (2017) and Antonakis et al. (2021).
Regarding the use of the GMM-SE in the robustness checks, following Jara et al. (2019), we address firm-level endogeneity issues by including lagged explanatory variables as instruments in our static models, which allows us to address the endogenous relationship between governance structures and earnings quality (Jara and López, 2014). As suggested by Reeb et al. (2012), we introduce all right-hand-side variables representing governance structures with one-period lags [21]. This GMM-SE is the primary technical approach we follow to mitigate the endogeneity issues that can arise from the simultaneity problem, which is the first-order concern for empirical research in corporate finance (Barros et al., 2019). Other sources of endogeneity are omitted variables and measurement errors. The GMM-Dif is our second-order approach to tackle the endogeneity issues.
As diagnostic tests, a panel-data Tobit model requires a normal distribution and homoskedasticity (Cotei and Farhat, 2011). In this case, the Breusch–Pagan contrast test checks that the estimated variance of the residuals is dependent on the values of the independent variables (e.g. heteroskedasticity). This test rejects the absence of firm-specific effects, indicating that the OLS regressions are inconsistent and consequently that firm- and industry-fixed-effects estimations are necessary. Additionally, the uncentered variance inflation factor (VIF) test determines whether the estimations suffer from autocorrelation. Although not reported to save space, in all cases the VIF is less than the 2.5 threshold beyond which autocorrelation is a concern. Regarding the GMM-SE regression outputs, the second-order autocorrelation test is reported [AR(2)], as is the Hansen contrast test of whether the instruments to control for the endogeneity were properly selected.
Moreover, the Lind and Mehlum (2010) contrast tests the posited concave relationship between board compensation and earnings quality. Lind and Mehlum (2010) develop their contrast to test U-shape (and, of course, the trivial extension for an inverse U-shape) relations and offer the necessary and sufficient conditions for the test in finite samples. Indeed, they argue that finding statistically significant both coefficients, the one of the corresponding variable and its quadratic term, in addition to identifying if the estimated extremum point is within the data range, is a weak criterion to justify humps and U shapes. They state that this criterion is neither sufficient nor necessary. It is insufficient as the extreme point may be close to an end point of the data range of the variable. Similarly, it is not generally necessary as the estimated coefficient of the corresponding nonsquared variable may be close to zero if its data rage takes positive and negative values, as is the case of BRem3 and BRem4 variables considered in this study. In fact, the authors state that the significance of the estimated coefficient of the corresponding quadratic variable alone is always a necessary condition in the test of a U shape (or inverted U shape) as is the case of Models (3), (4), (7) and (8) of Table A4 in this Appendix. As consistency check of the findings, although not reported in the tables, the Fieller (1954) confidence interval for the extrema points was estimated within the Lind-Mehlum test. In all the cases, the extrema points fell within the interval at the standard confidence levels.
Appendix 2. Construction of StarMine’s earnings-quality measures
StarMine’s earnings-quality algorithm is decomposed into firms’ accruals, cash flows and operating efficiency. The accruals measure is constructed as changes in operating assets and liabilities from four quarters ago to the present quarter, scaled by average assets. The operating–cash flow component is defined as the net of cash flow from operations and cash flow from investment. Cash flow is measured as annualized free cash flow scaled by average assets. Finally, the operating-efficiency measure is based on the return on assets. The return on assets is decomposed into a profit-margin subcomponent and an asset-turnover subcomponent, similarly to a DuPont analysis. StarMine evaluates asset turnover and profit margin against sector benchmarks due to the structurally differences various industries produce similar levels of return on assets. Profit margin is measured using the annualized operating profit margin as a percentage of annualized sales, while the total-asset turnover ratio is calculated using the ratio of annualized sales to average net operating assets. From these three measures, StarMine creates a ranking comparable across companies.
The various proxies of earnings quality identified by StarMine (EQ1, EQ2, EQ3, EQ4) range from 0 to 1, with higher values representing higher earnings quality. However, according to StarMine’s algorithm, companies are given a value of 1 if the quality of their earnings exceeds a certain threshold; and similarly, they are given a value of 0 if their earnings quality comes up short of a minimum specified value. So, by definition, these metrics are censored at both extremes. Therefore, we chose the panel Tobit model with fixed effects and robust standard errors, following Honoré (1992), to address the censored nature of the dependent variables. Because the EM measure does not suffer from censoring, the suggested GMM-SE technique is suitable as a robustness check of the main results.
Appendix 3. Panel composition and descriptive statistics
This section reports the composition and descriptive statistics of our panel data. Table A1 shows the panel composition of firms included in this study by year and industrial sector. Table A2 displays the summary statistics of the outcome measures, explanatory variables and control variables used in the model specification of this analysis. Table A3 provides a correlation matrix of the variables shown in Table A2 to examine the collinearity of similarly constructed measures. And Figure A1 shows the average female representation and average board compensation of directors of the Spanish firms in this sample over the study period.
Panel composition by industrial sector
| Industry sector | Years | Obs. | ||||||
|---|---|---|---|---|---|---|---|---|
| 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | Count | % | |
| Energy | 2 | 2 | 2 | 2 | 2 | 2 | 12 | 2 |
| Materials | 8 | 9 | 10 | 10 | 11 | 10 | 58 | 12 |
| Industrial | 16 | 17 | 20 | 21 | 18 | 15 | 107 | 22 |
| Consumer discretionary | 7 | 6 | 8 | 9 | 9 | 9 | 48 | 10 |
| Consumer staples | 6 | 7 | 6 | 5 | 7 | 5 | 36 | 7 |
| Health care | 7 | 7 | 8 | 10 | 10 | 7 | 49 | 10 |
| Information technology | 3 | 4 | 5 | 7 | 8 | 8 | 35 | 7 |
| Communication services | 5 | 4 | 6 | 6 | 7 | 7 | 35 | 7 |
| Utilities | 7 | 9 | 9 | 9 | 9 | 9 | 52 | 11 |
| Real estate | 3 | 9 | 11 | 10 | 13 | 13 | 59 | 12 |
| Total | 64 | 74 | 85 | 89 | 94 | 85 | 491 | 100 |
| Industry sector | Years | Obs. | ||||||
|---|---|---|---|---|---|---|---|---|
| 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | Count | % | |
| Energy | 2 | 2 | 2 | 2 | 2 | 2 | 12 | 2 |
| Materials | 8 | 9 | 10 | 10 | 11 | 10 | 58 | 12 |
| Industrial | 16 | 17 | 20 | 21 | 18 | 15 | 107 | 22 |
| Consumer discretionary | 7 | 6 | 8 | 9 | 9 | 9 | 48 | 10 |
| Consumer staples | 6 | 7 | 6 | 5 | 7 | 5 | 36 | 7 |
| Health care | 7 | 7 | 8 | 10 | 10 | 7 | 49 | 10 |
| Information technology | 3 | 4 | 5 | 7 | 8 | 8 | 35 | 7 |
| Communication services | 5 | 4 | 6 | 6 | 7 | 7 | 35 | 7 |
| Utilities | 7 | 9 | 9 | 9 | 9 | 9 | 52 | 11 |
| Real estate | 3 | 9 | 11 | 10 | 13 | 13 | 59 | 12 |
| Total | 64 | 74 | 85 | 89 | 94 | 85 | 491 | 100 |
Notes:
This table shows the number of firms by industry sector and year to compound the panel data. Additionally, it shows the number of indexed and nonindexed firms by industry sector
Appendix 4. Robustness of regression estimations
The following tables expand upon the primary analysis in Table 1. Table A4 alternates the metrics used for gender diversity and board remuneration. Table A5 tests the difference in the impact of gender diversity when substituting for the preferred measure of earnings quality (EQ1) its subcomponents (EQ2, EQ3 and EQ4). Table A6 modifies the preferred model specification to measure the robustness of the specification compared to a two-step GMM-SE estimator. Finally, Table A7 examines the sensitivity of these results using a traditional measure of earnings management (EM), while Table A8 uses the GMM in in first difference as final robustness check of the main findings. These robustness tables show consistent findings that gender diversity has a positive influence on earnings quality and that board remuneration has a concave relationship to earnings quality.



