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Transparency of Corporate Governance Through Public Information: Evidence from Spain

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06 October 2024

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07 October 2024

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Abstract
The measurement of corporate governance takes into account not only the profitability and solvency of the companies, but also the degree of information provided: its transparency. Corporate governance has become particularly relevant in recent years and one of the main problems is how to measure it properly. It is usually measured privately, either through audits or private verification companies. However, the public and mandatory information issued by these companies is rarely valued. This is where this research is new. The aim of this article is to develop a CGRI (Corporate Governance Ratio Index) to evaluate the transparency of corporate governance of companies using public and mandatory information. For this purpose, it is applicable to companies listed on the Spanish continuous market. This paper applies the index to the 35 most relevant companies in the Spanish continuous market. Subsequently, the paper asses the interrelation of the index with the main factors of corporate governance such as remuneration, monitoring and governance structure. By using a publicly available index, this study contributes to the transparency and accessibility of CG assessment, providing valuable insights into the effective corporate governance practices and thus the index helps to identify areas for improvement in the company itself and to observe the actions of its competitors.
Keywords: 
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1. Introduction

Corporate governance (CG) is a combination of policies, laws and instruments involved in the management and governance of a company (Solomon, 2020). It is a set of rules that ensures a relationship between a company and its shareholders is transparent and equitable (Fung, 2014; Solikhah and Maulina, 2021). It has a more direct impact on both employees and shareholders; shareholders perceive it through dividends and employees in their working conditions (Buallay et al., 2017). In this way, good CG makes companies attractive to future investors and increases market confidence (Guo and Kga, 2012) and can lead to better management as well as increase the transparency of the company (Forte et al., 2020).
The predominant theoretical framework in CG studies (Iskander and Chamlou, 2000; Beiner et al., 2006; Adams et al., 2010; Pande and Ansari, 2014) is agency theory (Jensen and Meckling, 1976). Its popularity is due to two characteristics (Daily et al., 2003).
i)
The first it reduces decision making to two groups –management and shareholders – with clearly defined divergent interests (Daily et al., 2003).
ii)
The second assumes that human beings are intrinsically selfish (Okibe, 2020) and therefore every rational individual pursues their own interests.
In recent decades, agency theory has studied these conflicts. Indeed, these are closely linked to the good governance of an organization and its efficient functioning (Eling and Marek, 2014). Most studies indicate that the mechanisms employed in CG limit the self-interest of managers to align with the interests of shareholders (Libson, 2020). In most companies, this internal mechanism is a properly structured board of directors (Ekanayake, 2018) and a pay structure that orients a manager toward shareholder interests (Kultys, 2016). Thus, corporate management can improve as long as the shareholders are oriented towards this improvement process.
Investor risk is reduced if internal CG mechanisms are correctly applied, thereby, increasing investment capital and improving corporate performance. This impact has been widely discussed, using different measures to analyse its effect on company performance, which can be operational-, market- or financial-based (Buallay et al., 2017). When analysing CG components, most studies focus on return on equity (ROE) as a financial measure and return on assets (ROA) as an operational measure or they analyse the components from a risk perspective, dividing them into financial, product and investment risk (Eling and Marek, 2014).
The aim of this paper is to establish an index based on public information, which determines the factors on which adequate CG depends. This paper develops the index through mandatory and transparent public reporting, so that it becomes applicable in other markets and countries. To demonstrate its applicability, this paper takes the most relevant companies in the Spanish continuous market that make up the IBEX35 stock market index (Iberian index). This paper evaluates the factors that positively impact the CG of these companies. To do so, it uses a large panel of data (64 criteria of 35 companies over two years) that allows the index to be extended to various productive sectors of the Spanish market.
Previous studies have focused on analysing the variables that affect CG through private indices. However, this paper analyses whether or not the variables referenced in the literature do or do not influence good corporate governance through an index based on the Annual Corporate Governance Report (ACGR). This report will be mandatory and required by the CNMV supervisor as well as by its European counterparts and will provide non-financial accounting information, providing value and differentiation to companies. This report indicates the degree of compliance with the recommendations given by the European Parliament and the European Council on good corporate governance. It is the basis for obtaining the necessary data to create the Corporate Governance Rating Index (CGRI). This index includes the data on Spanish companies in the continuous market that are obliged to report and published annually by the CNMV. Each company must respond to 64 criteria with qualitative answers. This paper develops a procedure to convert them into quantifiable data to create this index.
To address this objective, the paper is as follows. The second section reviews the indices that measure CG. The establishment of an index based on public information and the key factors that the literature considers relevant for good CG is located. Next, the paper presents the methodology of the research, determining for the Spanish case the variables that can help to measure IBEX35 companies’ CG, and the hypotheses are developed. The fourth section explains the modelling process as well as how each variable is measured. The fifth section discusses the results obtained through the empirical study and it finishes with the most relevant conclusions according to the data, tables and references used.

2. Index for Measuring Corporate Governance: A Literature Review

There is a substantial literature on finance and accounting, beginning with Gompers, Ishii and Metrick (2003). It develops various governance metrics from publicly available information based on large samples of U.S. and international companies and assesses the associations of good governance with various outcome variables. Today, transparency of in information is one of the criteria that determine good governance practices. Effective governance depends on transparency (Fung, 2014; Solikhah and Maulina, 2021), which entails providing shareholders with valuable information (Mechelli and Cimini, 2020) and instilling confidence in the companies they invest in (La Rosa et al., 2019; Shahid and Abbas, 2019). Moreover, transparency fosters increased investment in companies (Cai et al., 2019) and bolsters corporate reputation (Orzes et al., 2020) by conferring legitimacy in the eyes of stakeholders and society (Rossignoli et al., 2021).
In addition, effective governance processes are built upon principles of accountability (Ramaswamy et al., 2008; Solomon, 2020) and a steadfast commitment to long-term objectives (Salvioni et al., 2018). This framework fosters heightened performance (Todorovic, 2013; Lu and Wang, 2021), promotes growth (Tandukar, 2019), and ensures business stability (Wood and Small, 2019).
Optimal and transparent corporate governance is crucial not only to increase competitiveness (Davies and Schlitzer, 2008; Berinde, 2018; Laksito and Ratmono, 2021) and improve business efficiency, but also to safeguard the rights of shareholders and third parties. According to the European Union in 2014, ensuring optimal corporate governance is primarily the responsibility of the company itself (Rajoria, 2020). To uphold this, there have been established several standards at both national and EU levels to ensure compliance with essential criteria for good corporate governance. Additionally, various ratings are available to evaluate an entity’s corporate governance practices, as depicted in Table 1
In recent years, many European Union (EU) countries, including Spain, have adopted codes of good practice aimed at providing guidelines for listed companies to enhance the overall quality of corporate governance (CG). Spain has actively participated in this movement, achieving significant advancements in the field of good corporate governance. The Spanish government offers recommendations following the internationally recognized “comply or explain” principle to categorize each company’s specific CG within the corporate governance framework (CNMV, 2021). This principle requires companies to either comply with CGRI (Corporate Governance Report and Recommendations) requirements or provide explanations for non-compliance. While full compliance can signal positively to both the market (Solikhah and Maulina, 2021) and society (Bae et al., 2018), it might not always be the most suitable approach for a company from a CG standpoint (Rose, 2016). In certain instances, choosing not to implement a provision can enable more effective company management (Outa and Waweru, 2016; Bunget et al., 2020).
The utilization of publicly accessible good governance codes in conjunction with the “comply or explain” principle represents a valuable system for accomplishing key objectives of corporate governance. This approach is consistently implemented not only in major European Union nations but also in other developed countries (Beiner et al., 2006; Arora and Bodhanwala, 2018; Kahveci and Wolfs, 2019), underscoring its adaptability and potential to serve as a benchmark for best practices in corporate governance.
Moreover, the European Union explicitly endorses the applicability of this principle, as reaffirmed in the EU Green Paper on corporate governance of listed companies (European Commission, 2011). Except for the research by Corral et al., (2024), previous studies incorporating CG indices (Beiner et al., 2006; Arora and Bodhanwala, 2018; Kahveci and Wolfs, 2019) have typically assessed CG aspects independently. A notable contribution of this paper is the approach of considering these facets collectively.
Therefore, this paper develops the Index of Corporate Governance Reports and Recommendations (IGRC) used in this study from the recommendations given by public institutions, specifically the CNMV (2018). The 64 recommendations identified can be classified into three main blocks (Table 2):
The analysis assumes significance in the wake of recent financial scandals and crises, which have underscored the connection between CG and the design of remuneration systems, oversight mechanisms, and the structure of corporate boards.
The founding assumption of corporate governance theory postulates that mechanisms to match the interests of investors with the interests of management can exist. These mechanisms commonly entail the methods of compensating board members (referred to as internal incentives), the tools accessible to shareholders for monitoring board behavior, and the ownership structure (Jensen and Meckling, 1976).
Therefore, the proposed model elaborates on the impact of compensation (board remuneration), oversight (number of independent directors, number of board meetings, and number of female directors), and ownership structure (number of block shareholders and number of proprietary members) on a CG basis.
All three components play crucial roles in mitigating agency conflicts between investors and executives, which stem from the segregation of decision-making and decision control (Jensen and Meckling, 1976; Fama and Jensen, 1983; Chen, Steiner and Whyte, 1998; Eling and Marek, 2014). Moreover, the literature consistently identifies these factors as significant in achieving optimal governance, thus forming the basis of our hypotheses.

2.1. Compensation

Higher salaries are correlate with less risky decision-making and thus better corporate governance (Iatridis, 2018). Thus, compensation systems serve to align interests between investors and executives, with the level of executive remuneration compared to the market average being a critical factor. In a free market scenario where managers aim to maximize their utility, they are inclined to work for firms offering the highest utility, implying a positive correlation between pay levels and corporate governance (Grace, 2004; Milidonis and Stathopoulos, 2011).
However, the remuneration of directors and senior management may conflict with the interests of other stakeholders (Werner et al., 2005). Therefore, it must be controlled (Loderer and Martin, 1997). For directors, this entails imposing limits on remuneration and restricting share distribution (Filatotchev and Allcock 2010). In this sense, higher levels of corporate control appear to be associated with lower managerial compensation, and less linked to performance outcomes (Baixauli-Soler and Sánchez-Marín, 2015). In addition, executive cash compensation has a negative association with corporate governance, suggesting that this form of remuneration is not advisable (Iatridis, 2018). Moreover, the responsibilities carried out by governing board members align with the required qualifications and remuneration (Deschenes et al., 2015; Jacques et al., 2023).
The preceding considerations lead to the following hypothesis:
H1. 
There is no relationship between adequate GC and compensation (COM).

2.2. Monitoring

Board supervision is widely recognised as a crucial mechanism for corporate governance and shareholder influence (Michelon and Parbonetti, 2012). Numerous studies have investigated the importance of board independence for effective corporate governance oversight (Agrawal and Knoeber, 1996). There are even studies that interrelate board size with effective board monitoring and thus improved management (Kao et al., 2019) and adaptation to the business environment (Pucheta-Martinez and Gallego-Álvarez, 2019). Furthermore, the inclusion of young directors may challenge monitoring if there is a wide range of board members’ experience and destabilise management (Abu Qadam and Suwaidam, 2019).
On the other hand, it can affect the profitability of the company, since the contribution of independent members to effective supervision can improve firm performance (Chijoke-Mgbane et al., 2020). These members will ensure a more transparent management and provide objectivity to the information reported by the company (Forte et al., 2020). In many instances, a positive correlation has been found between more stringent oversight of company management and governance, particularly with boards comprised mostly of independent members (Agrawal and Knoeber, 1996), as well as more efficient meetings (Chen et al., 2015). Furthermore, the inclusion of female board members has been shown to influence oversight (Adamns and Ferreira, 2009), with their presence contributing to improved governance (Majumder et al., 2017). The preceding considerations lead to the following hypothesis:
H2. 
There is no positive relationship between adequate CG and its monitoring.

2.3. Structure of Boards

Previous research has indicated a positive correlation between blockholders and governance, particularly in terms of promoting efficiency (Huang and Wang, 2015). However, other studies suggest that concentrated control of a company’s capital by a few individuals can lead to conflicts with management and impede good governance practices (Eling and Marek, 2014). Some authors have criticized proprietary directors for focusing solely on personal enrichment through significant ownership stakes in the company’s capital, often at the expense of prioritizing the development of good governance within the organization (Douma et al., 2006; Grosman et al., 2019).
Therefore, it is imperative to verify these assertions.
H3. 
There is no positive relationship between adequate CG and governance structure.

3. Materials and Methods

3.1. Materials

In Spain, the requirement to disclose non-financial information came into force in 2017 and implemented in 2018. Consequently, the IAGC provides an analysis of the significance of good corporate governance (CG) practices in enhancing economic efficiency and bolstering investor confidence. It offers an overview of the evolution of CG regulations at the EU and international levels, a summary of key regulatory developments, and incorporates recommendations from codes of good governance, along with a description of CG guidelines for listed companies.
The database initially focuses on IBEX35-listed companies for the years 2018 and 2019. In order to mitigate the impact of the COVID-19 pandemic on the variables considered, data for the years 2020 and 2021 have not taken into account. There are four companies excluded from the analysis because they are not subject to Spanish law or because they contain biases or errors in their data. The data sources utilized for the sample include Annual Corporate Governance Reports (ACGRs) and annual reports from the CNMV. Using the 64 variables determined by the IAGC, the CGRI is obtained (Table 3) for a two-year period for the 31 selected companies listed on the IBEX35; that is totals 3,968 data.
Source: Authors.
This research also uses panel data with 558 observations from the companies’ accounting inform (2 years for 31 companies/years for nine variables/company) standardised in order to eliminate the time effect. These data come from public information from the CNMV. In order to carry out the study, the research transforms into variables. Thus, there are 4526 data used.
The paper uses a multiple linear regression to test the indicated hypotheses. In this way, the interaction of the variables is analysed, which determines the relationship of the three defined blocks as far as CG is concerned.
The 64 criteria established for the CGRI are the basis for measuring CG. Therefore, each recommendation has a specific weighting, in order to obtain qualitative corporate governance results, as the reports provide qualitative information. Based on the practices established by the CNMV, the research uses the following criteria for data transformation,
a) 1 if this recommendation is explained or fulfilled;
b) 0.75 if not applicable*;
c) 0.5 if partially compliant; and
d) 0 if is not compliant
*in the case of non-application and no concrete and concise explanation of the same, based on the good practice guidance for applying the comply-or-explain principle that was adopted on 15 July 2019 by CNMV.

3.2. Modelling the Corporate Governance

Rather than concentrating solely on three variables—remuneration, monitoring, and governance structure—the model has been expanded to include a broader array of variables. This enhancement involves calculating both the dependent and explanatory variables in a more comprehensive manner.

3.2.1. Executive Compensation or Remuneration (COM) (V1) (Loderer and Martin, 1997; Filatotchev and Allcock 2010)

The executive compensation or remuneration includes salaries, cash, and stock-based compensation. The total is given by the amount paid by the company to its executives, regardless of the number of directors (executive compensation). Similarly, total compensation per board member is calculated on a logarithmic basis.

3.2.2. Monitoring

Monitoring by the board of directors is pivotal for corporate governance, given that the board serves as the primary decision-making entity of the company. Effective board monitoring plays a crucial role in ensuring that decisions are made fairly and equitably, thereby safeguarding the interests of all shareholders. Torchia et al. (2015) investigated the correlation between board monitoring and the standard of corporate governance in Italian companies. Their study revealed a positive association between board oversight and the quality of corporate governance.
Board monitoring is a variable that the literature typically measures through
Number of council meetings throughout the year (Vafeas, 1999; Laksmana, 2008; Suteja et al. 2017; Ji et al. 2020). (NRC) (V2)
Ratio of independent board members: the percentage that the number of independent board members represents of the total number of board members. (IND) (V3) (Eling and Marek, 2014)
Ratio of executive board members (Yermack, 1996; Eisenberg et al., 1998; Huang and Wang, 2015): percentage of executives belonging to a company who are part of the top management. (EJ) (V4).
Ratio of female directors: percentage of the number of women in the total number of board members. (NCA) (V5).

3.2.3. Governance Structure

Management involvement in ownership serves to align the interests of management with those of shareholders, fostering a sense of shared objectives. Additionally, management can validate their decisions through the support of senior management, enhancing their legitimacy. However, in instances of highly concentrated company ownership, management may face increased scrutiny from shareholders. This heightened oversight can constrain management’s decision-making power and influence the formulation of policies and strategies, as shareholders closely monitor management’s actions and decisions.
Previous research has indicated a positive correlation between blockholders (ADV) (V6) and corporate governance, particularly in terms of enhancing efficiency (Huang and Wang, 2015). However, it is worth noting that an increase in the number of blockholders may lead to a larger pool of investors, potentially resulting in a lack of diversification in the company’s portfolio (Eling and Marek, 2014). Conversely, some authors (Douma et al., 2006; Grosman et al., 2019) have criticized blockholders for prioritizing profits for select shareholders, raising concerns about potential conflicts of interest.
The variables associated with blockholders enable the analysis of the number of significant company shareholders (Edmans and Holderness, 2017). In Spain, individuals or entities holding 5% or more of a company’s shares have the potential to influence company decision-making (DOM) (V7). Consequently, the analysis involves examining the proportions of shareholders with voting rights and the percentage of proprietary board members, as the latter may exert external influence on the company.

3.2.4. Control Variables

Finally, based on Beiner et al. (2006), two control variables are included as well as descriptive statistics for all variables (Table 4):
  • The CNMV (Comisión Nacional del Mercado de Valores/National Stock Market Commission), SECTOR (V8), serves as a controlling variable to assess which sectors yield the most favorable calculated corporate governance (CG) outcomes. For instance, it would be insightful to examine how companies in the service sector perform, considering their potential impact on other sectors. Each sector possesses unique characteristics, and it is valuable to investigate whether certain variables exert a more significant effect in specific sectors than in others. Thus, the research uses the CNMV’s sector classification (Table 4) to facilitate this analysis.
  • The variable Company size, SIZE (V9), is included as a control variable due to the different management practices observed in larger companies (Eling and Marek, 2014). The research defines this variable, commonly used in governance studies, as the logarithm of total assets. Compared to smaller companies, larger companies are often subject to greater scrutiny and analysis, which influences their governance practices (Eling and Marek, 2014). Therefore, incorporating company size as a control variable allows for a more accurate examination of the impact of other governance variables on company behavior and performance.
Source: Authors.

3.3. Empirical Model

The use of the statistical technique of multiple linear regression makes it possible to generate a linear model in which the value of the dependent variable (CGRI) is determined from a set of independent variables or predictors (Vh)
C G R I = α + h = 1 9 β h V h + ε
Where
α : Constant value. It is the intercept of the regression model.
β h : Linear regression coefficient. Represents the partial relationship of the h-th explanatory variable with the dependent variable. It is the average effect of a one-unit increase in the predictor variable Vh on the dependent variable CGRI; all other variables remain constant.
ε : It is the residual or error, i.e., the difference between an observed value and a value estimated by the linear model.
To identify all explanatory variables Vh that explain the relationship and degree of association with the dependent variable CGRI without any of them being a linear combination of the remaining variables, robust regression techniques such as iteratively reweighted least squares (Marx, 1996) can be employed. This method assigns weights to each observation based on whether they meet the assumptions underlying standard multiple regression.
The application of this model is justified by its widespread use in the literature, either for predicting the value of a dependent variable or for assessing the influence of each predictor on it. Consequently, this approach enables the analysis of the degree of influence of each variable on the CGRI of Spanish IBEX35-listed companies during 2018 and 2019.

4. IBEX35: Modelling Results

Based on the stepwise regression procedure and the goodness of fit of the data to the multiple linear regression model, the research proposes a model, featuring the highest multiple correlation coefficient (R). This model results from a systematic process of including or excluding variables based on their significance in explaining the variation in the dependent variable, CGRI. The aim is to construct a model that optimally captures the relationship between the dependent variable and the selected explanatory variables, resulting in the highest possible multiple correlation coefficient.
It is crucial to recognize that the magnitude of each partial regression coefficient depends on the units in which the corresponding predictor variable (Vh) is measured. Therefore, the magnitude of a coefficient alone does not necessarily indicate the importance of each predictor. The research uses standardized partial regression coefficients to assess the impact of each variable in the model. To do this, normalizing the predictor variables (subtracting the mean and dividing by the standard deviation) after adjusting the model (Table 5), the value of the coefficients is obtained. This standardization process allows for a fair comparison of the relative importance of each predictor variable in influencing the dependent variable, CGRI.
The model that interrelates enhanced corporate governance according to the requirements established in Spain causes executive members of the board of directors and independent members to influence the CG through the CGRI (Table 6). Thus, if these two components increase, so does governance and good management.
In this way, it is confirmed that meets the characteristics of IND (t = 5.375, p<0.05) and EJ (t = 2.528, p<0.05) which explain the independent variable ICCG more thoroughly, by means a positive linear association.
Likewise, the coefficient of determination (R2) shows that the variability explained by the model is 40.7%, which is very close to the adjusted coefficient of determination (Table 6). We can also observe the correlations between the variables (Table 7):
* Correlation is significant at the 0.05 level (bilateral). ** Correlation is significant at the 0.01 level (bilateral). Source: Authors.
The analysis of variance (ANOVA) identifies the variability explained by each of the predictors incorporated in the model (Table 8):
As in simple linear models or correlation studies, no matter how high the goodness of fit, if the F-test is not significant, the model is not valid because it is unable to explain the observed variance better than expected by chance.
The ANOVA test carried out in this study shows that, for model 2 (which integrates these 2 variables), the p-value associated with the F statistic is lower than the significance level (Table 8).
In this case, the p-value is less than 0.05; therefore, tis research finds that the characteristics of IND and EJ significantly describe the degree of relevance they attach to CGRI. Therefore, H2 is rejected: There is no positive relationship between adequate CG and the monitoring of board members (IND). Thus, the predictor variables that enter the equation are the latter IND and EJ (Table 9).
C G R I = 0.750 + 0.003 · I N D + 0.002 · E J + ε
On the contrary, the following variables are excluded (Table 9) from the rest of the hypotheses: SECTOR (t = -0.348, p > 0.05), SIZE (t = 0.666, p > 0.05), NRC (t = 1.152, p > 0.05), NCA (t = 1.480, p > 0.05), ADV (t = -0.19, p > 0.05), DOM (t = -0.077, p > 0.05) and COM (t = 0.130, p > 0.05).

5. Conclusions

To date, previous studies have primarily concentrated on analyzing the variables that influence corporate governance (CG) using private indices. However, this paper represents a pioneering effort by offering a comprehensive analysis of the variables—defined throughout the literature—that either have or lack a significant influence on good corporate governance. This research is performed through a Corporate Governance Report and Recommendations Index (CGRI) specifically designed for a quantitative measurement of CG, sourced from a public institution such as the CNMV in Spain. By using a publicly available index, this study contributes to the transparency and accessibility of CG assessment, providing valuable insights into the determinants of effective corporate governance practices and thus improve the business performance of the companies. The index helps to identify areas for improvement in the company itself and to observe the actions of its competitors.
In contrast to the literature on corporate governance (CG) for IBEX35-listed companies, the results obtained in this study diverge significantly from studies conducted in other countries. While at the international level there are numerous variables that influence CG to a greater or lesser extent, this study underscores the uniqueness of the Spanish context. This leads to the conclusion that corporate governance and its management must be adapted to the habitat in which the company operates.
The main conclusion in the case of the IBEX35 companies studied, is that good CG is mainly influenced by the number of independent directors and the number of executive directors. Independent directors are commonly believed to positively impact organizational performance by advocating for practices that, through effective monitoring, mitigate potential blockholder appropriation and thus promote the development of good CG. It is important to remember that it also improves the profitability of the company, as other authors have shown.
The analysis indicates that the remuneration received by directors and executives, or the governance structure, does not significantly influence the development of good corporate governance among IBEX35 companies. Instead, it underscores the significance of monitoring conducted by independent and executive members, utilizing control mechanisms, which positively influences good CG. This underscores the importance of effective oversight and governance practices, particularly in fostering transparency and accountability within Spanish companies.
Moreover, regarding monitoring, the study finds that the number of annual board meetings and the incorporation of women into senior management do not significantly affect governance within these entities. In the case of board meetings, it is the acceptance of the recommendations established by the CNMV as well as the frequency of the meetings that influences them. These recommendations should focus on assessing the productivity of these meetings and the efficiency of decisions made during them.
Similarly, regarding the incorporation of women into senior management, the absence of specific recommendations directly addressing this issue complicates the evaluation of compliance. Even if companies report information on the number of women in decision-making roles, they may perceive this recommendation as fulfilled, regardless of whether they meet the minimum percentage of women required. Therefore, it is suggested that recommendation 14 from the CNMV be split into two separate recommendations—one promoting the incorporation of women into senior management positions and the other addressing the approval of boards of directors’ selection policies. This separation would enable a more accurate measurement of compliance with the inclusion of women in decision-making positions.
Spanish listed companies are mandated to document their level of compliance in an Annual Corporate Governance Report (ACGR) and, if necessary, provide an explanation for any non-compliance with specific recommendations. The adequacy and thoroughness of these explanations, intended to justify instances of non-compliance, should be subject to auditing by external and independent professionals. This ensures that shareholders, investors, and the broader market can assess them accurately and make informed judgments regarding the company’s governance practices. Such audits contribute to transparency and accountability, fostering trust and confidence among stakeholders in the company’s operations and decision-making processes.

Author Contributions

Conceptualization, J.C-L, A.G, E.T and J.I.D.; methodology, J.C-L, A.G, E.T and J.I.D.; software, J.C-L, A.G, E.T and J.I.D.; validation, J.C-L, A.G, E.T and J.I.D.; formal analysis, J.C-L, A.G, E.T and J.I.D.; investigation, J.C-L, A.G, E.T and J.I.D.; resources, J.C-L, A.G, E.T and J.I.D.; data curation, J.C-L, A.G, E.T and J.I.D.; writing—review and editing, J.C-L, A.G, E.T and J.I.D.; visualization, J.C-L, A.G, E.T and J.I.D.; supervision, J.C-L, A.G, E.T and J.I.D.; project administration, J.C-L, A.G, E.T and J.I.D.; funding acquisition, J.C-L, A.G, E.T and J.I.D.
All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Grupo de Investigación Consolidado ClioBasque Eusko Jaurlaritza/Gobierno Vasco EJ/GV, grant number IT 1523-22.

Data Availability Statement

The data used for this paper can be found at the following web addresses of the Comisión Nacional del Mercado de Valores/National Stock Market Commision (CNMV). For accounting data: https://www.cnmv.es/ipps/default.aspx . For data concerning Corporate Governance: https://www.cnmv.es/portal/Publicaciones/PublicacionesGN.aspx?id=21

Acknowledgments

The authors acknowledge the administrative support provided by the Department of Financial Economics I of the University of the Basque Country.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. Private ratings for assessing corporate governance.
Table 1. Private ratings for assessing corporate governance.
Authors Index Explanation
Khanchel (2007) Standard and Poor’s List of 80-100 factors. Grouped into three categories: ownership structure and investor relations, financial transparency and disclosure, and board and management structure and processes.
Bauer et al. (2004) Eurotop 300 del Financial Times Stock Exchange (FTSE) Based on some 300 different criteria. These criteria can be grouped into four broader categories: shareholder rights and obligations; range of takeover defences; CG disclosure and board structure and functioning.
Klapper y Love (2004) Credit Lyonnais Securities Asia-CLSA Ltd. The ranking takes into account seven categories: discipline, transparency, independence, accountability, responsibility, impartiality and social conscience. Each category has a weight of 0.15, except for the last one, which has a weight of 0.10.
Brown y Caylor (2006) Shareholder Services The elements divided into four equally weighted categories (0.25): shareholder rights, board of directors, external directors, and disclosure and transparency.
Source: Authors.
Table 2. Categories of corporate governance management in CNMV.
Table 2. Categories of corporate governance management in CNMV.
General Aspects General Meeting of Shareholders Board
Statutory limitations Transparency Responsibility
Company listing Attendance and participation Structure and composition
Monitoring of recommendations Attendance fees policy Functioning and organization of the board
Shareholders’ and block shareholders’ meetings Remuneration of directors
Share issues Sustainability, environmental, and social aspects
Source: Authors through CNMV, 2018.
Table 3. Descriptive statistics of the CGRI.
Table 3. Descriptive statistics of the CGRI.
N Minimum Maximum Media Deviation
CGRI 62 ,76171875 ,97656250 ,9175907258 ,05021261905
Source: Authors.
Table 4. Descriptive Statistics for the variables used in the analysis.
Table 4. Descriptive Statistics for the variables used in the analysis.
CGRI COM NRC IND EJ NCA ADV DOM SIZE
Mean 0.91 15.80 11.38 50.57 16.76 0.24 31.46 24.22 23.74
Maximum 0.98 17.95 18.00 71.43 40.00 0.43 72.29 69.23 28.05
Minimum 0.76 13.40 5.00 15.38 5.56 0.00 0.00 0.00 18.64
Standard Dev. 0.06 1.04 3.09 11.82 8.41 0.09 22.60 17.37 2.08
Source: Authors.
Table 5. Summary of the modelc.
Table 5. Summary of the modelc.
Model R R2 R2 adjusted Standard error
1 .571a 0.326 0.312 0.043138839
2 .638b 0.407 0.382 0.040903068
a. Predictors: (Constant), IND
b. Predictors: (Constant), IND, EJ
c. Dependent variable: CGRI
Source: Authors.
Table 6. Complete model and final (Stepwise) model Coefficientsa.
Table 6. Complete model and final (Stepwise) model Coefficientsa.
Complete Model Non-standard coefficients Standard coefficients t Sig.
β Error β
1 (Constant) .451 .155 2.917 .006
SIZE -.002 .005 -.049 -.352 .726
SECTOR -.004 .003 -.149 -1.156 .254
NRC .005 .002 .325 2.214 .033
IND .002 .001 .556 2.546 .015
EJ .003 .001 .364 1.958 .057
NCA .190 .085 .297 2.223 .032
ADV .000 .000 -.177 -1.126 .267
DOM .001 .001 .183 .695 .491
COM .015 .008 .267 1.924 .062
a. Variable dependent: ICGC
Final Model Non-standard coefficients Standard coefficients t Sig.
β Error β
1 (Constant) .791 .026 30.970 .000
IND .002 .001 .571 4.822 .000
(Constant) .750 .029 25.741 .000
IND .003 .000 .609 5.375 .000
EJ .002 .001 .286 2.528 .015
Source: Authors.
Table 7. Pearson Correlation between the variables used in the analysis.
Table 7. Pearson Correlation between the variables used in the analysis.
ICGC SIZE SECTOR NRC IND EJ NCA ADV DOM COM
ICGC 1.000
SIZE .274* 1.000
SECTOR .108 .191 1.000
NRC .205 .268* .280* 1.000
IND .558** .414** .144 .225 1.000
EJ .282* .192 .256* -.067 -.019 1.000
NCA .284* .166 .125 -.053 .437** -.113 1.000
ADV -.109 -.256* -.030 .262* -.192 .035 -.241 1.000
DOM -.550** -.194 -.073 .014 -.632** -.467** -.314* .407** 1.000
COM +.118 .442** -.022 -.169 .034 .227 .025 .247 -.174 1.000
* Correlation is significant at the 0.05 level (bilateral). ** Correlation is significant at the 0.01 level (bilateral). Source: Authors.
Table 8. ANOVAa.
Table 8. ANOVAa.
Model Sum of squares gl Root mean square F Sig.
1 Regression ,043 1 .043 23.253 .000b
Waste ,089 48 .002
Total ,133 49
2 Regression ,054 2 .027 16.128 .000c
Waste ,079 47 .002
Total ,133 49
a. Dependent variable: CGRI
b. Predictors: (Constant), IND
c. Predictors: (Constant), IND, EJ
Source: Authors.
Table 9. Excluded variablesa.
Table 9. Excluded variablesa.
Model β t Sig. Partial correlation
1 SECTOR .012b .099 .921 .014
SIZE .084b .674 .504 .098
NRC .097b .790 .434 .114
EJ .286b 2.528 .015 .346
NCA .138b 1.046 .301 .151
ADV -.095b -.784 .437 -.114
DOM -.309b -2.056 .045 -.287
COM .204b 1.759 .085 .248
2 SECTOR -.041c -.348 .729 -.051
SIZE .079c .666 .509 .098
NRC .134c 1.152 .255 .167
NCA .184c 1.480 .146 .213
ADV -.019c -.154 .878 -.023
DOM -.077c -.352 .726 -.052
COM .130c 1.102 .276 .160
a. Dependent variable: CGRI
b. Predictors: (Constant), IND
c. Predictors: (Constant), IND, EJ
Source: Authors.
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