This study explored how board diversity affects environmental, social, and governance (ESG) performance in the Italian banking sector. Specifically, this study examined whether the presence of specific corporate governance (CG) characteristics (board diversity) in Italian Cooperative Credit banks is related to ESG dimensions.
The authors examined a sample of 247 Italian Cooperative Credit banks for the period 2017–2021 and developed an econometric model by applying unbalanced panel data with firm fixed effects and controls per year. To verify the research hypotheses, the authors analyzed board diversity in terms of board attributes variables (size, gender diversity, age, activity, independence and corporate social responsibility/sustainability committee (CSR) and measured ESG dimensions using the ESG score provided by Refinitiv.
The findings suggest that board size, independence and the existence of a CSR/sustainability committee positively affect banks' ESG performance, while no significant relationship between board average age and ESG performance was found. The study also explored how the critical mass of women on a board affects ESG performance by testing the positive impact of gender diversity on ESG dimensions only up to a certain threshold of female directors.
This study is highly relevant to managers and investors who consider ESG issues in their decision-making processes. The findings support regulators by offering insights into ways to improve ESG performance through the specific design and application of governance mechanisms.
From a practical perspective, this investigation has implications for both practitioners and regulators, suggesting that chief executive officers (CEOs) and managers should pay more attention to CG aspects to improve ESG performance and that policy-makers should give greater consideration to these aspects of CG in their efforts to enhance ESG performance.
This study offers an in-depth analysis of banks' ESG practices and attempts to bridge the gap in the literature on ESG in the Italian banking industry. This study is the first to investigate the relationship between CG variables and ESG dimensions in this context.
1. Introduction
In recent years, environmental, social and governance (ESG) performance has become increasingly important for banks and financial institutions. ESG issues are not only ethical questions but will soon become economic questions owing to their direct and significant influence an economy's financial stability (Adams, 2013; Buallay, 2019; Donaldson and Preston, 1995; Ferrero-Ferrero et al., 2015; Jitmaneeroj, 2016). ESG is an important factor in corporate strategies for competitive advantage, innovation and opportunities and has become a key indicator of management competence. It is increasingly important for boards of directors (BoDs) to be knowledgeable about ESG to address long-term sustainability risks and integrate them into corporate strategies and business models. A company's success principally depends on its BoD (García-Sánchez et al., 2014) because directors emphasize business ethics and corporate responsibility, improve company culture, oversee the achievement of strategic goals and approve the system of corporate governance (CG) (Aguilera et al., 2006; Jo and Harjoto, 2011; Villanueva-Villar et al., 2016). In this sense, the BoD plays a central role in integrating sustainability into business strategy and aligning the interests of the company and shareholders toward ESG goals (de Andres and Vallelado, 2008; De Haan and Vlahu, 2016; Birindelli et al., 2018). The characteristics and composition of the BoD are crucial in defining strategic decision-making concerning ESG dimensions and in developing socially responsible behaviors toward social manageability execution and social sustainability performance (Forbes and Milliken, 1999; Michelon and Parbonetti, 2012; Post et al., 2011, 2015; Setó-Pamies, 2015; Cuadrado-Ballesteros et al., 2017; Awan, 2019a, b).
Therefore, we investigated how particular CG characteristics relate to ESG performance. The specific purpose of this study is to answer the following research question: what is the relationship, if any, between board diversity and ESG performance? In doing so, this study extends the traditional research on CG and offers a preliminary snapshot of the potential relationship between board diversity and ESG performance in 247 Italian banks through a panel data regression analysis of the sample over a five-year period (2017–2021). While there is growing interest in the banking literature with respect to ESG, the body of literature concerning the influence of CG aspects on ESG dimensions is still relatively small. This study fills this gap by examining how board diversity influences banks' ESG performance. Following a review of the extant literature on the present topic of interest, we noted a gap in the existing research as no studies in the Italian banking setting have addressed the issue of board diversity and ESG. Moreover, a sizeable amount of empirical research has focused solely on composite ESG scores, which offer only a narrow view of the sample businesses' sustainability practices (Chams and García-Blandón, 2019). As a result, delving into the individual pillars of the ESG framework can reveal significant information such as which aspects receive higher priority in the sample banks: is it the environment, social concerns or governance?
More specifically, in line with the existing literature (Post et al., 2011; Zhang et al., 2013; Setó-Pamies, 2015; Rao and Tilt, 2016b; Kaymak and Bektas, 2017), our study investigates the impacts of a comprehensive set of board characteristics on ESG scores across a sample of Italian banks: board size, gender diversity, age, activity, independence and corporate social responsibility (CSR)/sustainability committees. The main empirical results confirmed that the board's size, independence, activity and presence of a CSR/sustainability committee positively influence a bank's ESG performance, while no significant relationship was found between the board's average age and ESG performance. The correlation between gender-balanced boards and ESG performance was positive; however, the influence of female directors on ESG performance became non-linear once a critical mass of women was reached. Our findings underscore how BoD diversity enhances learning capabilities and increases relational governance, thus moving toward developing a more sustainable way of managing environmental and social performance (Awan, 2019a, b) through the coordination, sharing and processing of knowledge, information and core competencies between directors (Awan et al., 2018). This study contributes to the banking literature in many ways. First, the existing literature on the relationship between board diversity and ESG performance primarily focuses on non-financial firms, whereas this study concentrates specifically on the banking sector. Second, to the best of our knowledge, only a few recent studies have analyzed this topic in the banking sector (Birindelli et al., 2018; Shakil et al., 2021; Gurol and Lagasio, 2022). Further, we contribute to the literature on ESG in the Italian context (Perrini et al., 2006) because this study is the first to examine the relationship between CG variables and ESG dimensions by using the ESG score provided by Refinitiv.
The remainder of this paper is organized as follows. Section 2 provides a literature review and the research hypotheses. Section 3 describes the sample, variables and the methodology used to estimate the econometric model. Section 4 presents and discusses the empirical results, and Section 5 presents the conclusions, limitations and implications of the study, as well as suggestions for future research.
2. Literature review
An organization's ESG score is directly related to its board's sense of responsibility toward business, society and the environment since the task of the BoD is to ensure a connection between the business strategy and the external environment (de Andres and Vallelado, 2008; Carter et al., 2010). Hence, the effect of board structure on ESG dimensions has attracted the attention of researchers and practitioners. Various studies have investigated the relationship between CG and sustainability performance (Tamimi and Sebastianelli, 2017; Birindelli et al., 2018) and there is increasing interest in the impact of board composition on CSR (Bear et al., 2010; Ortiz-de-Mandojana and Aragon-Correa, 2015; Shaukat et al., 2016). However, little is known about how board diversity affects ESG performance (Zhang et al., 2013; Trireksani and Djajadikerta, 2016; Rao and Tilt, 2016a). Moreover, few studies explore the relationship between a bank's board structure (its characteristics and composition) and ESG performance in the European banking sector (Birindelli et al., 2018; Cucari et al., 2018; Shakil et al., 2021).
The variables most widely used to study the impact of CG on ESG dimensions are board size, board age, share of women on the board of directors (board gender diversity), share of independent directors on the board (board independence), board activity and the existence of a CSR/sustainability committee (Giannarakis, 2014). We develop our hypotheses for each board characteristic in the following subsections.
2.1 Board size
The recent CG literature related to banks has investigated the relationship between board size and ESG dimensions (Velte, 2016), and previous studies have focused on the influence of board size on ESG disclosure (Cheng and Courtenay, 2006; Jizi et al., 2014). For example, in a meta-analysis specifically centered on ESG reporting, Lagasio and Cucari (2018) demonstrated that increasing the number of directors on the board is positively related to ESG voluntary disclosure. Esa and Ghazali (2012) verified the positive impact of board size on the extent of Malaysian firms' CSR disclosures. However, Giannarakis (2014) did not find that board size has a significant impact on ESG disclosure.
Generally, in line with resource dependence theory, a large number of board members is an indicator of diversity because a larger board is equipped with more expertise and different management perspectives (Zahra and Pearce, 1989). On the one hand, in accordance with the legitimation perspective, although small boards might have a low degree of gender diversity and less variety in education level, expertise and stakeholder representation, they are characterized by high levels of commitment, teamwork and coordination. Furthermore, small boards entail extensive responsibilities and heavy workloads for directors, who might, therefore, carry out their oversight role less successfully (Jizi et al., 2014). According to Husted and de Sousa-Filho (2019), the idea that having many board members offers management different strategic points of view is more acceptable than thinking that they may have difficulty making decisions. In this sense, many directors consider it advantageous to have a broad range of views on sustainability practices and diversified ESG expertise on the board.
Our first hypothesis agrees with the findings of previous studies that board size positively affects sustainability practices (Htay et al., 2012 Jizi et al., 2014; Jizi, 2017). In the prior banking literature, Birindelli et al. (2018) also showed that board size positively influences ESG performance in European and American banking sectors. In line with resource dependence theory, we assume that board size is positively related to ESG performance. Thus, we postulate the following:
There is a positive relationship between board size and ESG performance.
2.2 Board gender diversity
Board gender diversity decisively influences ESG performance (Velte, 2016). According to the dependence resource theory (Nguyen et al., 2015), the relationship between female directors on boards and ESG activities is due to the various attributes of the women themselves. Women appear to be more sensitive towards sustainability initiatives (Birindelli et al., 2018; Samara et al., 2019) than men owing to certain female psychological traits (e.g. helpfulness and sensitivity), their educational background and professional experience (Kyaw et al., 2017; Manita et al., 2018).
Hence, skilled women may be more responsive than men to ESG issues (Williams, 2003; Arayssi et al., 2016; Velte, 2016). However, in this regard, the results of prior studies are mixed (Zaman et al., 2020). Some researchers found both positive and negative impacts of board gender diversity on firms' ESG performance (Cucari et al., 2018; Husted and de Sousa-Filho, 2019; Arayssi et al., 2020; Wasiuzzaman and Wan Mohammad, 2020). For example, Mc Guinness et al. (2017) while Disli et al. (2022) confirmed that the presence of female directors increases CSR performance. By contrast, Manita et al. (2018) found no significant relationship between board gender diversity and ESG disclosure. Birindelli et al. (2018) demonstrated that the relationship between the women-to-men ratio of a bank's BoD and its ESG disclosure exhibits an inverted U-shape. Although the prior literature on board gender diversity and ESG performance in the context of banks is limited and the empirical results are mixed, our research expectations are positive, following the resource dependence theory. Hence, we theorize the following:
There is a positive relationship between board gender diversity and ESG performance.
Based on critical mass theory, the presence of many women on the board can shape a firm's sensitivity towards social and environmental issues. However, previous studies have yielded conflicting results due to the existence of a non-linear relationship between board gender diversity and sustainability performance. For example, Deschênes et al. (2015) proved there is a negative relationship between sustainability practices and the participation of female directors, while Kiliç et al. (2015) and Glass et al. (2016) found a low statistically significant positive effect, and Khan (2010) and Alazzani et al. (2017) verified no significant correlation. A non-linear relationship signifies that the presence of at least three women on the board is required to significantly influence board activity and change decision-making within the BoD. Hence, board gender diversity positively influences social and environmental performance only when a significant threshold (critical mass) for women in the BoD is reached. For example, Cabeza-García et al. (2017) showed that a critical mass of at least three female directors increases CSR disclosures. In line with this evidence, Manita et al. (2018) found that the relationship between board gender diversity and ESG disclosure is not statistically significant when there are fewer than three female directors on the board. According to the critical mass theory, we advance the following assumption:
There is a positive relationship between a critical mass of women on the board of directors and ESG performance.
2.3 Board age
The average age of directors, or board age, has drawn attention in the previous literature, given that it is one of the most important observable board diversity characteristics. It expresses the influence of diverse generations (and consequently the impact of different values, motivational goals, habits, culture and experiences) on the decision-making approach adopted by directors. Resource dependence theory favors heterogeneous board structures over homogeneous ones because different age groups on the board can be beneficial for improving ESG attitudes (Handajani et al., 2014). However, there is little empirical evidence proving that age diversity of directors leads to better corporate performance (Ali et al., 2014) and few empirical studies exploring the relationship between board age and CSR disclosure exist. Giannarakis (2014) analyzed the relationship between average board age and CSR disclosure and found no significant correlation between the two variables. Similarly, Cucari et al. (2018) demonstrated that is there is no significant relationship between the average age of BoDs and ESG disclosure.
Since an increase in average age indicates a homogeneous board structure, according to the resource dependence theory, we hypothesize the following:
There is a negative relationship between board age and ESG performance.
2.4 Board activity
Board activity is generally measured by the number of BoD meetings held annually (Laksmana, 2008). There is no consensus in the literature on the effect of board meeting frequency on performance. Some studies have highlighted the positive implications of frequent board meetings, indicating that a high number of meetings reflects board efficiency (Kent and Stewart, 2008). In line with the agency theory, the frequency of board meetings facilitates the supervision of a company's activities and greater monitoring of management (Vefeas, 1999). Ricart et al. (2005) observed that frequent board meetings positively impacts sustainability performance because directors set aside additional time for sustainability-related discussions. Hence, we formulate the following hypothesis:
There is a positive relationship between board activity and ESG performance.
2.5 Board independence
The variable most frequently used by researchers to describe the structure of a board is board independence as it is a key characteristic that ensures that corporate strategic policies remain geared towards stakeholders' interests and expectations (Ortas et al., 2017; Cucari et al., 2018). In line with the stakeholder theory, the presence of independent directors on boards encourages management activities to protect stakeholders and reduce conflicts of interest between them and the BoD (Patelli and Prencipe, 2007). According to the agency theory, independent directors facilitate the effective oversight of board practices as they can make more objective judgments on management performance. Several studies (Jo and Harjoto, 2011; Jizi et al., 2014) show that independent managers are more inclined to disclose ESG information to reduce asymmetric information problems. Moreover, many authors have suggested that BoDs are highly engaged in CSR reporting and investment when independent directors encourage the implementation of sustainable initiatives (Cheng and Courtenay, 2006; Chau and Gray, 2010).
Some studies on the influence of board independence on CSR disclosure have verified that a positive relationship between board independence and CSR reporting exists (Ahmed et al., 2006; Khan et al., 2012; Garas and ElMassah, 2018), while others have found that the presence of non-executive and independent directors on boards has a negative impact on social and environmental disclosure (Haniffa and Cooke, 2005; Mallin et al., 2013). In line with the former, Husted and de Sousa-Filho (2019) tested and confirmed the positive effect of independent directors on ESG performance, whereas Ortiz-de-Mandojana and Aragon-Correa (2015) suggested that independent directors drive the development of a firm's environmental sustainability only under certain conditions. In contrast, other studies have documented no significant relationship between the proportion of independent directors on a board and an organization's sustainability practices (Walls and Berrone, 2017).
Previous studies report strong but inconsistent empirical evidence of the impact of board independence on CSR performance in the banking sector. For example, positive results were found by Barako and Brown (2008), Jizi et al. (2014) and Kiliç et al. (2015), while no significant correlation was demonstrated by Hossain and Reaz (2007). In contrast, Birindelli et al. (2018) confirmed there is a negative and significant relationship between the independent director ratio and banks' CSR scores. As most prior studies prove that the independence of directors on the board is positively associated with ESG scores, we propose the following:
There is a positive relationship between board independence and ESG performance.
2.6 CSR/sustainability committee
In recent years, banks have increasingly demonstrated their intention to make sustainability a key business strategy by establishing CSR/sustainability committees to implement sustainability initiatives. The presence of such a committee on the BoD proves the board's commitment to sustainable practices (Hussain et al., 2018) and the directors' interest in enhancing the implementation of socially and environmentally responsible activities (Liao et al., 2015). According to the stakeholder theory, sustainability committees usually support BoDs in realizing sustainable projects, handling the company's CSR activities and monitoring sustainability risks (Mahmood et al., 2018). In line with this perspective, many authors have found that the presence of a CSR/sustainability committee is positively related to the extent of sustainability disclosures (Liao et al., 2015; Helfaya and Moussa, 2017). For example, Amran et al. (2014) proved that a CSR/sustainability committee improves the quality of sustainability reporting and involves shareholders in a company's ethical culture. Similarly, Cucari et al. (2018) showed that the presence of a CSR committee increases the ESG disclosure score provided by the Bloomberg database. Finally, Spitzeck (2009) demonstrated that the CSR committee favors the achievement of high volumes of CSR activities, thus leading to higher performance. By contrast, few studies have found a negative relationship between the presence of a CSR/sustainability committee and CSR performance (Michelon and Parbonetti, 2012).
Based on the literature review, we propose the following:
There is a positive relationship between the establishment of a CSR/sustainability committee and ESG performance.
To provide some insights into the current literature regarding the relationship between board diversity and ESG performance, we summarize the most important studies on this topic to date in Table 2.
Explanation of variables
| Variable | Description measure | Reference | Expected effect on ESG performance |
|---|---|---|---|
| Dependent variable | |||
| ESG variable (source: Refinitiv) | |||
| ESG performance (ESG_perf) | Comprehensive scoring of the environment, social and governance performance by the weighted average of the ESG scores and ESG controversies (captured from global media sources) (see Table 1) | ||
| Independent variables | |||
| Board diversity indicators | |||
| Board size (B_size)* | Total number of directors on the bank's board | Said et al. (2009), Amran et al. (2014), Jensen (1993), De Andres et al. (2005), Laksmana (2008) | Positive |
| Board gender diversity (B_gend) | Percentage of women on the board of directors (number of female directors divided by total number of board members) | Adams and Ferreira (2009), Amran et al. (2014), Cordeiro et al. (2020), Husted and de Sousa-Filho (2019), Cucari et al. (2018), Galbreath (2016, 2018); Rao and Tilt (2016a, b), Barako and Brown (2008) | Non-linear |
| Board mass of gender diversity (B_mgend) | Dummy variable that takes value 1 if the bank's board has at least three women, 0 otherwise | Post et al. (2011), Fernansez-Feijoo et al. (2014), Liu (2018), Ben-Amar et al. (2017), Shoham et al. (2017) | Positive |
| Board age (B_age) | Board average age (the time-varying age in years of directors in board) | Giannarakis (2014), Cucari et al. (2018) | No effects |
| Board activity (B_act)* | Natural logarithm of the total number of board meetings during the year | Disli et al. (2022) | Positive |
| Board independence (B_ind) | Percentage of independent directors on the bank's board | Ahmed et al. (2006), Chau and Gray (2010), Lim et al. (2007), Rao and Tilt (2016a, b) | Positive/Negative |
| CSR/sustainability committee (CSR_com) | Dummy variable equal to 1 if the bank has a CSR committee, 0 otherwise | Hussain et al. (2018), Liao et al. (2015) | Positive |
| Control variables | |||
| Bank size (SIZE)* | Natural logarithm of Total assets of the bank (Euro) | Setó-Pamies (2015), Helfaya and Moussa (2017) | Positive |
| Leverage (LEV) | The ratio of Tier 1 capital to Total Assets (proxy for the Basel 3 Leverage ratio) | Helfaya and Moussa (2017) | Positive |
| Return on equity (ROE) | Net income divided by the value of total shareholders' equity | Setó-Pamies (2015), Helfaya and Moussa (2017) | Positive/Negative |
| GDP per capita (GDP)* | Gross Domestic Product (GDP) per capita | Fernansez-Feijoo et al. (2014), Hu and Scholtens (2014) | Positive/Negative |
| Variable | Description measure | Reference | Expected effect on ESG performance |
|---|---|---|---|
| Dependent variable | |||
| ESG performance (ESG_perf) | Comprehensive scoring of the environment, social and governance performance by the weighted average of the ESG scores and ESG controversies (captured from global media sources) (see | ||
| Independent variables | |||
| Board size (B_size)* | Total number of directors on the bank's board | Positive | |
| Board gender diversity (B_gend) | Percentage of women on the board of directors (number of female directors divided by total number of board members) | Non-linear | |
| Board mass of gender diversity (B_mgend) | Dummy variable that takes value 1 if the bank's board has at least three women, 0 otherwise | Positive | |
| Board age (B_age) | Board average age (the time-varying age in years of directors in board) | No effects | |
| Board activity (B_act)* | Natural logarithm of the total number of board meetings during the year | Positive | |
| Board independence (B_ind) | Percentage of independent directors on the bank's board | Positive/Negative | |
| CSR/sustainability committee (CSR_com) | Dummy variable equal to 1 if the bank has a CSR committee, 0 otherwise | Positive | |
| Control variables | |||
| Bank size (SIZE)* | Natural logarithm of Total assets of the bank (Euro) | Positive | |
| Leverage (LEV) | The ratio of Tier 1 capital to Total Assets (proxy for the Basel 3 Leverage ratio) | Positive | |
| Return on equity (ROE) | Net income divided by the value of total shareholders' equity | Positive/Negative | |
| GDP per capita (GDP)* | Gross Domestic Product (GDP) per capita | Positive/Negative | |
Note(s): *Natural logarithmic transformations of the numerical (non index) variables
Source(s): Table by authors
3. Data and methodology
3.1 Sample selection and data sources
This study investigates the relationship between board diversity and ESG dimensions in Italy. The Italian banking sector is comprised of credit cooperatives, private banks and state-owned banks. Banks can be large, small, regional, or national and are sometimes structured as joint-stock companies (also listed). We constrain our sample to banks located in Italy that operate in the corporate form of a cooperative society. The selection procedure resulted in a sample of 247 Cooperative Credit banks that were all active and geographically localized in Italy, according to the Bankit Bulletin statistics up to December 31, 2021.
Our initial data set of banks had to fulfill the following criteria:
Italian banks (either private or state owned);
Organized as cooperative companies;
Active during the period 2017–2021;
Not turned off or merged with other banks during the research period; and
Not Italian branches of foreign banks.
We eliminated banks that did not satisfy these prerequisites (Menicucci and Paolucci, 2022) and finally arrived at our sample by considering banks with five consecutive years of ESG performance data collected from the Refinitiv (also called Refinitiv Eikon, hosted by Thomson Reuters) database. Based on the data availability, the final sample to be inspected included 247 Italian banks and consisted of 1,482 bank-year observations from 2017 to 2021.
This dataset offers three key advantages for studying the relationship between board diversity and the ESG dimensions. First, the investigation was not prejudiced by specific regulations because the selected banks were subjected to the same regulatory and governance backgrounds. Second, our sample is large and homogenous as the selected banks perform similar activities within the same regulatory environment under the supervision of the Bank of Italy and the European Central Bank. The sample consists of small, medium and large banks predominantly involved in corporate, investment and commercial banking activities with similar funding opportunities. Third, all banks had a composite management structure and followed a similar business models.
The data on ESG performance was collected from the Refinitiv database, a trusted international databank that contains the most complete ESG data, counting more than 450 different ESG metrics that are historically available. This database has a strong and transparent procedure for accessing ESG data available on its official website, is frequently used by researchers, and has been utilized in prior studies on the banking sector (Esteban-Sánchez et al., 2017; Gangi et al., 2019; Miralles-Quirós et al., 2019; Shakil et al., 2019; Bătae et al., 2021).
3.2 Dependent variables
ESG_perf reflects banks' weighted average of ESG scores and controversies (captured from global media sources) to provide a comprehensive evaluation of the sustainable and societal impact of corporate behavior. Following the prior literature (Bătae et al., 2021), we use the ESG score by Refinitiv as a proxy for the ESG performance of Italian banks. The overall ESG score is expressed as a percentage ranging from 0% to 100%. The independent variable ESG_perf is measured using three ESG pillars (the environmental pillar (ENV), the social pillar (SOC) and the governance pillar (GOV)) (De Villiers et al., 2017); hence, in accordance with previous banking studies (Peni and Vähämaa, 2012; Esteban-Sánchez et al., 2017; Shakil et al., 2019; Buallay et al., 2021), the combined indicator (ESG_perf) is a comprehensive scoring of ESG performance. A pillar is the weighted average of ten correlated dimensions, and each ESG dimension is composed of individual elements. The Refinitiv database gave a calculated score for each ESG dimension. Table 1 below lists the ESG data used in this study. The following discussion describes the dimensions relating the ESG pillars in the Refinitiv database, all of which are relevant to this study.
Definition of ESG variables
| Variable | Description measure | ESG predictor |
|---|---|---|
| ESG performance, ESG pillars and ESG dimensions (source: Refinitiv) | ||
| ESG performance (ESG_perf) | Weighted average of the ESG scores and ESG controversies (captured from global media sources) | It is a combined indicator of ESG pillars (i.e. the environmental pillar (ENV), the social pillar (SOC), the governance pillar (GOV)), discounted for ESG controversies |
| ESG Pillar | Description Measure | ESG Dimensions |
| Environmental (ENV) | Environmental performance measures a company's capacity to reduce environmental emissions, to efficiently use natural resources in the production processes and to support the research and development of eco-efficient products and services | It is based on three dimensions: ENV_Ru (Resource use efficiency), ENV_Em (Emission and waste reduction), ENV_In (Environmental innovation) ENV_Ru = bank's efficiency in reducing the use of materials, energy or water and capacity to find more eco-efficient solutions for the business processes ENV_Em = bank's commitment and effectiveness in reducing environmental emissions and waste in operational activities ENV_In = bank's capacity to reduce the environmental burdens and costs for its clients and to create new opportunities for eco-designed products and services |
| Social (SOC) | Social performance measures a company's capacity to generate trust and loyalty in its workforce, to respect the fundamental conventions of human rights, to be a good citizen, to protect public health, to respect business ethics and to create value-added products and services | It is based on four dimensions: SOC_Wf (Workforce), SOC_Hr (Human rights), SOC_Com (Community), SOC_Prd (Product responsibility) SOC_Wf = bank's effectiveness towards job satisfaction, safe and healthy workplace, while developing both equal and diversity opportunity SOC_Hr = bank's effectiveness in respecting fundamental human rights conventions SOC_Com = bank's commitment to being a good citizen, respecting business ethics and protecting public health SOC_Prd = bank's capacity to offer high quality products and services, regarding the customers' health and Safety, data privacy and integrity |
| Governance (GOV) | Corporate governance performance measures a company's capacity to act in the best interest of its shareholders through management systems and processes (structure and functions of the board of directors, compensation policy, etc.) | It combines three dimensions: GOV_Mo (Management and oversight), GOV_Shr (Shareholders rights), GOV_Csr (CSR strategy) GOV_Mo = bank's commitment and effectiveness in following corporate governance principles GOV_Shr = bank's effectiveness in treating its shareholders in an equal manner GOV_Csr = bank's way to incorporate social and environmental dimensions in its decision-making processes |
| Variable | Description measure | ESG predictor |
|---|---|---|
| ESG performance, ESG pillars and ESG dimensions (source: Refinitiv) | ||
| ESG performance (ESG_perf) | Weighted average of the ESG scores and ESG controversies (captured from global media sources) | It is a combined indicator of ESG pillars (i.e. the environmental pillar (ENV), the social pillar (SOC), the governance pillar (GOV)), discounted for ESG controversies |
| ESG Pillar | Description Measure | ESG Dimensions |
| Environmental (ENV) | Environmental performance measures a company's capacity to reduce environmental emissions, to efficiently use natural resources in the production processes and to support the research and development of eco-efficient products and services | It is based on three dimensions: ENV_Ru (Resource use efficiency), ENV_Em (Emission and waste reduction), ENV_In (Environmental innovation) |
| Social (SOC) | Social performance measures a company's capacity to generate trust and loyalty in its workforce, to respect the fundamental conventions of human rights, to be a good citizen, to protect public health, to respect business ethics and to create value-added products and services | It is based on four dimensions: SOC_Wf (Workforce), SOC_Hr (Human rights), SOC_Com (Community), SOC_Prd (Product responsibility) |
| Governance (GOV) | Corporate governance performance measures a company's capacity to act in the best interest of its shareholders through management systems and processes (structure and functions of the board of directors, compensation policy, etc.) | It combines three dimensions: GOV_Mo (Management and oversight), GOV_Shr (Shareholders rights), GOV_Csr (CSR strategy) |
Source(s): Table by authors
Refinitiv contains 34 indicators relating to the environmental pillar score (ENV), clustered in three dimensions: resource use efficiency (ENV_Ru), emission and waste reduction (ENV_Em) and environmental innovation (ENV_In). The first, Env_Ru, comprises the following elements: energy- and water-efficiency policies, environmental management systems, renewable energy use ratios, supply chain management and monitoring, and green buildings. The second, Env_Em, refers to emissions policies and targets, total CO2 emissions, e-waste reduction, waste management, environmental restoration, climate change opportunities and staff transportation impact reduction. The third, Env_In, groups data related to environmental products, clean energy products, environmental project financing and environmental assets under management.
The Refinitiv Eikon dataset comprises 40 indicators referring to the social pillar score (SOC), clustered in four dimensions: workforce (Soc_Wf), human rights (Soc_Hr), community (Soc_Com) and product responsibility (Soc_Prd). The first, Soc_Wf, comprises data on training and development policies, health and safety policies, equivalent opportunities, diversity, flexible working hours, employee turnover and salary gaps. The second, Soc_Hr, reports data on human rights, freedom of association and child labor. The third, Soc_Com, contains data on anti-money laundering, bribery, fair competition, business ethics (widely regulated by the European Banking Authority in the banking sector), community lending, and community involvement. Finally, Soc_Prd covers indicators of customer satisfaction, quality management systems and data privacy policies (part of the General Data Protection Regulation).
The governance pillar (GOV) includes three dimensions: management and oversight (Gov_Mo), stakeholder rights (Gov_Shr) and CSR strategy (Gov_Csr). First, GOV_Mo identifies the aggregate scores of corporate board characteristics. In this regard, it compiles data related to corporate boards (structure policy, functions, size, attendance, affiliations, average tenure, non-executive and independent members, cultural and gender diversity, background and skills), compensation (the compensation committee and its independence, sustainability incentives, shareholders' approval of stock compensation plans, policy improvement tools), CEO-chairperson separation, the nomination committee and its independence, remuneration packages depending on stakeholders' returns, the succession plan, the audit committee independence and external consultants. The second, GOV_Shr, comprises data on specific policies and shareholder equal rights, shareholder votes on executive pay, voting cap percentage, veto power or golden shares, director election majority requirement, anti-takeover devices, auditor tenure and the non-audit-to-audit fee ratio. Finally, Gov_Csr is an aggregate score that includes CSR sustainability reporting, the CSR sustainability committee and stakeholder engagement.
3.3 Independent variables
We use four independent variables to measure data related to board diversity (B_div). In line with prior research and theories concerning B_div and ESG issues in the banking sector, the independent variables included in the econometric model are the total number of directors on the bank's board (B_size), the percentage of female directors sitting on the board (B_gend), the critical mass of women on the board (B_mgend), the average age of the board (B_age), the number of board meetings (B_act), the share of independent board members (B_ind) and the establishment of a CSR/sustainability committee (CSR_com). In the model, B_mgend is a dummy variable coded as 1 if there are more than three females on the board and 0 otherwise and CSR_com is a dummy variable that equals 1 if the bank has a committee and 0 otherwise (Said et al., 2009; Birindelli et al., 2018; Cucari et al., 2018). Table 2 presents the definitions and formulas of the variables.
3.4 Control variables
To avoid model misspecification, we controlled for additional variables that could influence the ESG scores. In accordance with previous studies (Husted and de Sousa-Filho, 2019; Shakil et al., 2019; Albitar et al., 2020; Arayssi et al., 2020), four control variables were included because of their significant effects on banks' ESG performance. In line with the existing literature on ESG (Setó-Pamies, 2015; Helfaya and Moussa, 2017; Jizi, 2017), we identified the following widely studied bank-specific control variables: bank size (Size), bank leverage (Lev) and return on equity (Roe). Bank size (Size) was calculated as the natural logarithm of total assets (Platonova et al., 2018; Nizam et al., 2019). In previous banking studies, some authors reported that large banks easily attract cheaper resources because they are more diversified across different segments and more scrutinized by the community and media. Consequently, large banks have access to more capital to invest in CSR activities (Setó-Pamies, 2015; Helfaya and Moussa, 2017). We also consider Lev, measured as Tier 1 capital to total assets (Brammer and Millington, 2008; Harjoto et al., 2015; Velte, 2016) and Roe as an indicator of bank profitability (Harjoto et al., 2015; Cordeiro et al., 2020).
Finally, we included one country-specific control variable: GDP growth (Gdp). We used macroeconomic specification as a control variable to overcome endogeneity issues that often appear in economics-based sustainable reporting research in the form of simultaneity, reverse causality and correlated variables (Nikolaev and Van Lent, 2005; Larcker and Rusticus, 2010; Contractor et al., 2016). In line with the previous literature on the banking sector (Wu and Shen, 2013; Shen et al., 2016), we used Gdp as a representative variable of macroeconomic dynamics. Table 2 below provides a description of all control variables.
3.5 Model specifications
To test the research hypotheses, we applied a linear regression model using the ordinary least squares (OLS) method because of its general quality of minimized bias and variance (Greene, 2004). First, we examined the relationships between the model variables for the same observation year. We estimate the model as follows:
where i refers to the bank, t refers to the year, and is a stochastic error term. B_div is defined as the above-described alternative board variables: board size (B_size), board gender diversity (B_gend), board mass of gender diversity (B_mgend), board age (B_age), board activity (B_act), board independence (B_ind) and the CSR/sustainability committee (CSR_com). To quantify the effect of board diversity proxies on ESG performance, we also controlled for variables that could potentially affect a bank's ESG appetite (control variablesi,t). Table 2 lists all the variables.
To capture the impact of B_div on ESG performance over time as effects appear and to reduce any endogeneity problems, the B_div variables of any year are related to the ESG measure of the following two years. We used one- and two-year lags because the effect of the B_div variables on the ESG performance score can take time to be effectively integrated into a bank's ESG performance. Hence, the explanatory variables were lagged by one and two years, respectively. The models are presented as follows:
Following Baltagi (2001), we applied panel data, which provide more variability and less collinearity among the variables. We controlled for individual heterogeneity using a fixed-effects estimation with standard errors clustered at the bank level. The selection of a fixed effects model rather than a random effects model was tested using the Hausman test for all specifications (Baltagi, 2001). We also used the Breusch-Pagan test to check for residual heteroscedasticity. We removed firm-level heterogeneity using cross-sectional mean deviation data (Greene, 2004). Given the dynamic nature of our model, least squares estimation methods generate biased and inconsistent results. Hence, we used dynamic panel estimation techniques to address the biases of our estimates. To manage issues related to endogeneity, exogenous changes from mandatory executive retirements in board characteristics were identified by applying difference-in-difference estimation techniques, as in Berger et al. (2014).
4. Findings and discussion
This section examines the impact of the B_div variables on ESG performance. First, we investigated the descriptive statistics and correlations. Then we analyzed the main estimation results and present our robustness checks.
4.1 Descriptive statistics
Table 3 presents the descriptive statistics of the main variables for the entire sample.
Descriptive statistics of the variables
| Min | Max | Mean | Median | Std. Dev | |
|---|---|---|---|---|---|
| Dependent variables | |||||
| ESG performance (ESG_perf) | 30.1725 | 89.4332 | 61.3474 | 61.3646 | 13.3738 |
| Independent variables | |||||
| Board size (B_size) | 23 | 7 | 14.2504 | 14.0645 | 4.6770 |
| Board gender diversity (B_gend) | 0 | 0.4267 | 0.3247 | 0.1025 | 0.3978 |
| Board mass of gender diversity (B_mgend) | 0 | 1 | 0.3153 | 0.3796 | 0.3059 |
| Board age (B_age) | 52.4462 | 65.4060 | 58.4242 | 58.4006 | 2.4342 |
| Board activity (B_act) | 1.089 | 3.677 | 2.037 | 2.044 | 0.526 |
| Board independence (B_ind) | 0 | 1 | 0.6085 | 0.6166 | 0.2741 |
| CSR/sustainability committee (CSR_com) | 0 | 1 | 0.5840 | 0.5999 | 0.4976 |
| Control variables | |||||
| Bank size (Size) | 6.8634 | 9.4309 | 7.8265 | 7.8028 | 0.6542 |
| Leverage (Lev) | 0.0176 | 0.2129 | 0.0747 | 0.0853 | 0.0455 |
| Return on equity (Roe) | −2.7792 | 0.7187 | 0.0416 | 0.0678 | 0.2042 |
| GDP per capita (Gdp) | −12.5154 | 9.5076 | 0.0827 | 0.1595 | 3.6763 |
| Min | Max | Mean | Median | Std. Dev | |
|---|---|---|---|---|---|
| Dependent variables | |||||
| ESG performance (ESG_perf) | 30.1725 | 89.4332 | 61.3474 | 61.3646 | 13.3738 |
| Independent variables | |||||
| Board size (B_size) | 23 | 7 | 14.2504 | 14.0645 | 4.6770 |
| Board gender diversity (B_gend) | 0 | 0.4267 | 0.3247 | 0.1025 | 0.3978 |
| Board mass of gender diversity (B_mgend) | 0 | 1 | 0.3153 | 0.3796 | 0.3059 |
| Board age (B_age) | 52.4462 | 65.4060 | 58.4242 | 58.4006 | 2.4342 |
| Board activity (B_act) | 1.089 | 3.677 | 2.037 | 2.044 | 0.526 |
| Board independence (B_ind) | 0 | 1 | 0.6085 | 0.6166 | 0.2741 |
| CSR/sustainability committee (CSR_com) | 0 | 1 | 0.5840 | 0.5999 | 0.4976 |
| Control variables | |||||
| Bank size (Size) | 6.8634 | 9.4309 | 7.8265 | 7.8028 | 0.6542 |
| Leverage (Lev) | 0.0176 | 0.2129 | 0.0747 | 0.0853 | 0.0455 |
| Return on equity (Roe) | −2.7792 | 0.7187 | 0.0416 | 0.0678 | 0.2042 |
| GDP per capita (Gdp) | −12.5154 | 9.5076 | 0.0827 | 0.1595 | 3.6763 |
Note(s): Panel data for the period 2017–2021
Source(s): Table by authors
The average ESG_perf level of the banks examined was 61.3646 with a maximum of 89.4332. According to the standards of the score definition, the sample banks' sustainability performance for the period 2017–2021 was very satisfactory. Italian banks maintained good ESG performance, although they also had a high level of ESG controversies given that the average ESG controversy score was 42.75%. B_ind reached an adequate average value (0.6085) and the maximum value was 1. By contrast, the average representation of women on boards (B_gend) still seems low, considering that some bank boards do not appoint any female directors (the minimum value is equal to zero). On average, 32.4% of the Italian bank directors were female. Table 3 also shows that 31% of the banks had at least three female managers on average. Table 3 presents the bank-specific control variables. The bank size (size), mean leverage (Lev) and return on equity (Roe) were 7.82665, 0.0747 and 0.0416, respectively.
Pearson correlations were calculated to check for multicollinearity among continuous variables (Hair et al., 2006). Table 4 shows the correlation coefficients between the variables included in the regression model.
Pearson correlations
| Variables | ESG_perf | B_size | B_gend | B_mgend | B_age | B_act | B_ind | CSR_com | Size | Lev | Roe | Gdp | VIF |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ESG_perf | 1.0000 | 0.7772** | 0.5694*** | 0.4755*** | 0.1965 | 0.6753** | 0.0897 | 0.6677*** | 0.7570*** | −0.2247*** | 0.0884 | 0.2127*** | 1.26 |
| B_size | 1.0000 | −0.1377 | 0.3082*** | 0.0587 | 0.0124 | −0.3744*** | 0.1718*** | 0.2615*** | −0.2232*** | 0.0046 | −0.4296*** | 1.14 | |
| B_gend | 1.0000 | 0.7347*** | −0.0776 | −0.1050*** | 0.1286** | 0.3354*** | 0.4730*** | −0.1514*** | 0.1411** | 0.1313** | 1.65 | ||
| B_mgend | 1.0000 | −0.1877 | −0.2077** | 0.0227 | 0.3122*** | 0.3987*** | −0.1920*** | 0.0887 | −0.0766 | 1.34 | |||
| B_age | 1.0000 | 0.0744 | 0.2866 | 0.0623 | 0.3555 | −0.0557 | 0.0445 | −0.2488 | 1.44 | ||||
| B_act | 1.0000 | −0.2113* | 0.1549** | 0.1099*** | 0.2102*** | −0.2223** | −0.0067 | 1.17 | |||||
| B_ind | 1.0000 | 0.0177 | 0.1107 | 0.3654*** | 0.1316** | 0.4565*** | 1.35 | ||||||
| CSR_com | 1.0000 | 0.4349*** | −0.3332*** | −0.0766 | −0.1626*** | 2.55 | |||||||
| Size | 1.0000 | −0.2645*** | 0.0263 | −0.0777 | 2.43 | ||||||||
| Lev | 1.0000 | 0.1960*** | 0.1762*** | 1.09 | |||||||||
| Roe | 1.0000 | 0.1825 | 2.29 | ||||||||||
| Gdp | 1.0000 | 1.68 |
| Variables | ESG_perf | B_size | B_gend | B_mgend | B_age | B_act | B_ind | CSR_com | Size | Lev | Roe | Gdp | VIF |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ESG_perf | 1.0000 | 0.7772** | 0.5694*** | 0.4755*** | 0.1965 | 0.6753** | 0.0897 | 0.6677*** | 0.7570*** | −0.2247*** | 0.0884 | 0.2127*** | 1.26 |
| B_size | 1.0000 | −0.1377 | 0.3082*** | 0.0587 | 0.0124 | −0.3744*** | 0.1718*** | 0.2615*** | −0.2232*** | 0.0046 | −0.4296*** | 1.14 | |
| B_gend | 1.0000 | 0.7347*** | −0.0776 | −0.1050*** | 0.1286** | 0.3354*** | 0.4730*** | −0.1514*** | 0.1411** | 0.1313** | 1.65 | ||
| B_mgend | 1.0000 | −0.1877 | −0.2077** | 0.0227 | 0.3122*** | 0.3987*** | −0.1920*** | 0.0887 | −0.0766 | 1.34 | |||
| B_age | 1.0000 | 0.0744 | 0.2866 | 0.0623 | 0.3555 | −0.0557 | 0.0445 | −0.2488 | 1.44 | ||||
| B_act | 1.0000 | −0.2113* | 0.1549** | 0.1099*** | 0.2102*** | −0.2223** | −0.0067 | 1.17 | |||||
| B_ind | 1.0000 | 0.0177 | 0.1107 | 0.3654*** | 0.1316** | 0.4565*** | 1.35 | ||||||
| CSR_com | 1.0000 | 0.4349*** | −0.3332*** | −0.0766 | −0.1626*** | 2.55 | |||||||
| Size | 1.0000 | −0.2645*** | 0.0263 | −0.0777 | 2.43 | ||||||||
| Lev | 1.0000 | 0.1960*** | 0.1762*** | 1.09 | |||||||||
| Roe | 1.0000 | 0.1825 | 2.29 | ||||||||||
| Gdp | 1.0000 | 1.68 |
Note(s): *,**, and *** denotes level of significance at the 0.10, 0.05 and 0.01 levels, respectively
Source(s): Table by authors
The correlation matrix (Table 4) shows the important relationships between the main variables of this study. ESG_perf was positively related to B_age, B_size, B_act, CSR_com and size. Specifically, the results confirmed that the highest correlation was between ESG_perf and B_size, whereas the correlation between ESG_perf and Gdp was the lowest. In addition, B_gend was positively correlated with ESG_perf (p < 0.05). These relationships reveal that the banks that are the most engaged in ESG issues appoint more female directors to their boards and often establish committees dedicated to sustainability. Interestingly, the B_gend variable was positively related to both Size and Roe, signifying that banks with more female directors on their boards are larger and more profitable than those with gender-balanced boards. Similarly, B_ind was positively correlated with a bank's economic performance (Roe).
The matrix (Table 4) shows that the correlations between the variables were not strong. The value of the variance inflation factor (VIF test) demonstrates that multicollinearity was not a serious issue because it was far below the critical value. The correlation coefficients of the variables were lower than the threshold level of 0.90, demonstrating non-significant multicollinearity among the variables (Hair et al., 2006). The correlation coefficients indicate that the employed model was reliable and satisfactory because there was no high correlation between the variables, even at its maximum degree.
We performed estimates using six bank board variables and examined the effects of these explanatory variables on Italian banks' ESG performance. The results are summarized in Table 5.
Panel regression results for predictors with robust standard errors
| Model 1 ESG_perf Coeff. (p-value) No lag | Model 2 ESG_perf Coeff. (p-value) One-year lag | Model 3 ESG_perf Coeff. (p-value) Two-years lag | |
|---|---|---|---|
| B_size | 0.0773(0.0398) | 0.0548**(0.03687) | 0.0665**(0.0356) |
| B_gend | 0.1778*(0.0897) | 0.2892**(0.1404) | 0.4469**(0.1842) |
| B_mgend | −0.2189*(0.1626) | −0.3157*(0.1626) | 0.6936*(0.3629) |
| B_age | 0.0133(0.0177) | 0.0136(0.0167) | 0.0124(0.0165) |
| B_act | 0.0584**(0.0175) | 0.0752*(0.124) | 0.0555*(0.1767) |
| B_ind | 0.0554*(0.0306) | 0.0593**(0.0299) | 0.0625**(0.0307) |
| CSR_com | 0.0339*(0.0234) | 0.0386*(0.0223) | 0.0368*(0.0237) |
| Size | 0.0175***(0.0057) | 0.0147***(0.0066) | 0.0167***(0.0046) |
| Lev | −0.3905(0.7493) | −0.4077*(0.7534) | −0.3331*(0.7422) |
| Roe | 0.0146**(0.0067) | 0.0148**(0.0059) | 0.0156**(0.0053) |
| Gdp | 0.0155(0.1613) | 0.0011(0.1383) | 0.0057(0.1478) |
| RegressionF | 18.73*** | 15.88*** | 17.25*** |
| R2 within | 0.4315 | 0.2369 | 0.1266 |
| R2 between | 0.5709 | 0.4986 | 0.0132 |
| R2 overall | 0.4599 | 0.3705 | 0.0017 |
| Wald χ2 | 79.12** | – | – |
| Hausman χ2 | 23.68 | 29.97* | 37.72 |
| Fixed/Random effects | Fixed | Fixed | Fixed |
| Model 1 ESG_perf | Model 2 ESG_perf | Model 3 ESG_perf | |
|---|---|---|---|
| B_size | 0.0773(0.0398) | 0.0548**(0.03687) | 0.0665**(0.0356) |
| B_gend | 0.1778*(0.0897) | 0.2892**(0.1404) | 0.4469**(0.1842) |
| B_mgend | −0.2189*(0.1626) | −0.3157*(0.1626) | 0.6936*(0.3629) |
| B_age | 0.0133(0.0177) | 0.0136(0.0167) | 0.0124(0.0165) |
| B_act | 0.0584**(0.0175) | 0.0752*(0.124) | 0.0555*(0.1767) |
| B_ind | 0.0554*(0.0306) | 0.0593**(0.0299) | 0.0625**(0.0307) |
| CSR_com | 0.0339*(0.0234) | 0.0386*(0.0223) | 0.0368*(0.0237) |
| Size | 0.0175***(0.0057) | 0.0147***(0.0066) | 0.0167***(0.0046) |
| Lev | −0.3905(0.7493) | −0.4077*(0.7534) | −0.3331*(0.7422) |
| Roe | 0.0146**(0.0067) | 0.0148**(0.0059) | 0.0156**(0.0053) |
| Gdp | 0.0155(0.1613) | 0.0011(0.1383) | 0.0057(0.1478) |
| RegressionF | 18.73*** | 15.88*** | 17.25*** |
| R2 within | 0.4315 | 0.2369 | 0.1266 |
| R2 between | 0.5709 | 0.4986 | 0.0132 |
| R2 overall | 0.4599 | 0.3705 | 0.0017 |
| Wald χ2 | 79.12** | – | – |
| Hausman χ2 | 23.68 | 29.97* | 37.72 |
| Fixed/Random effects | Fixed | Fixed | Fixed |
Note(s): N = 247 (number of Italian banks). ƩiTi.N = 1,482 (number of bank-year observations). The robust standard errors of the estimated coefficients reported in parentheses are clustered at the bank level. ***p < 0.01, **p < 0.05, *p < 0.1
Source(s): Table by authors
Table 5 presents the estimation results for Equations (1), (2), and (3). Our findings reveal that B_div influences ESG data in all models (no lag, one-year lag and two-year lag). For the one-year and the two-year lags, the results were similar to the same-year (no lag) results.
Our findings show a positive relationship between B_size and ESG scores, thus confirming Hypothesis 1. In Models 2 and 3, the empirical results are significant and consistent with the correlation data (p < 0.05). Econometric models demonstrate that larger bank boards lead to better ESG performance, validating the results of most of the prior literature on the banking sector (Baselga-Pascual et al., 2018; Birindelli et al., 2018; Gurol and Lagasio, 2022). It is more likely that larger boards are composed of directors with different types of expertise in ESG and a greater propensity to favor a sustainable culture. Furthermore, larger boards exercise oversight activities more effectively, inspire a broader vision of strategic goals and, from this standpoint, also encourage management to develop sustainability performance.
Table 5 shows the significant and positive effect of B_gend on the ESG_perf of Italian banks in all models. B_gend is a positive predictor at a significance level of 0.1 and 0.5 in Model 1 and in Models 2 and 3, respectively. In accordance with the critical mass theory, we also included the predictor B_mgend in the models. The regression model equations suggest that once the board of directors achieves a critical mass of three women, a greater proportion of women will have a negligible impact on the ESG score (Schwartz-Ziv, 2017). Hence, in any specification, the results do not corroborate Hypothesis 3, which predicts that the critical mass of female directors on a board positively affects a bank's ESG performance (critical mass theory). In particular, the relationship between female directors on the board and the extent of a bank's sustainability performance is an inverted U-shaped function because the parabola enters a downward phase once a critical mass of women is reached. This result is confirmed by the quadratic term B_gend (data not tabulated for brevity), which accounts for potential non-linearities and endogenously limits the threshold for female directors. Hence, having a greater number of female directors on the board beyond the cited threshold does not imply improved ESG performance, thus supporting the theoretical underpinning of the critical mass theory. The findings from the regression models imply that only gender-balanced boards positively influence a bank's ESG performance because the interactions inside the group grow when the size of a minority group hits a threshold (at least three). The results from the regression models also support the resource dependence theory (Kyaw et al., 2017; Manita et al., 2018), suggesting that female directors' intellectual and relational traits are critical resources for banks that aim to achieve high ESG performance.
In line with the prior literature (Huang, 2013; Giannarakis, 2014; Gurol and Lagasio, 2022), the results indicate no significant association between B_age and ESG_perf. Hence, our findings demonstrate that banks with better ESG performances do not necessarily have older boards. In this regard, the board should consist of directors with a balance of expertise, skills and diversity, who, regardless of age, jointly possess the appropriate experience and competence with respect to the size, complexity and risk profile of the bank. Regarding board activity, the estimated coefficients of B_act are positive and statistically significant in all models (5% in Model 1and 10% in Models 2 and 3). Thus, Hypothesis 4, which proposed that a greater frequency of board meetings promotes ESG performance, is supported by the empirical evidence. We believe that frequently held board meetings considerably reduce the negative effects of ESG controversies and encourage sustainability-related behaviors.
Our findings that board independence (B_ind) positively affected ESG_ Perf contrasts with the findings of previous studies (Haniffa and Cooke, 2005; Ortiz-de-Mandojana and Aragon-Correa, 2015; Walls and Berrone, 2017; Baselga-Pascual et al., 2018). Nevertheless, empirical evidence supports the idea that independent directors promote both shareholder and stakeholder interests in ESG matters. In this regard, the presence of many independent members on a bank's BoD results in increased expertise, experience and reputation, which are crucial factors in a bank's sustainability performance.
In our analysis, the establishment of a CSR/sustainability committee (CSR_com) is positively correlated (p < 0.1) with ESG_perf in all models. Based on our empirical analysis, the presence of such a committee proves the bank's CSR commitment to making sustainability a key strategic issue in the governance system. These findings are in line with the previous literature, according to which a board with a CSR/sustainability committee is expected to be more environmentally and socially responsive (Liao et al., 2015). In accordance with the stakeholder theory, the results confirm that a CSR committee helps banks build credibility for sustainability subjects and improves the legitimacy of stakeholders regarding this specific topic as the members of such committees have skills, experience and knowledge focused on CSR issues (Amran et al., 2014).
Most studies confirm the resource dependence and stakeholder theories as conceptual frameworks explaining the relationship between board diversity and CSR (Rao and Tilt, 2016a, b). The interaction between these theories suggests that a diversified board is more likely to represent diverse stakeholder attitudes towards CSR.
Table 5 presents the data related to the control variables. In line with several prior studies, the findings highlight that both bank size (Size) and economic performance (Roe) have a positive and statistically significant effect on ESG_perf, at 0.01 and 0.5, respectively. Hence, the empirical evidence reveals that high sustainability performance is mostly achieved by large and more profitable banks (Baselga-Pascual et al., 2018) because they possess ample resources and personnel to invest in ESG activities. By contrast, a bank's Lev is negatively related to ESG_perf in Models 2 and 3; thus, banks with high Lev show low ESG performance. These findings regarding Lev are consistent with those of previous studies (Velte, 2016; Manita et al., 2018; Arayssi et al., 2020).
To verify the robustness of the empirical results, we conducted a robustness test to ascertain whether the relationship between board diversity and ESG_perf is affected by banks' market capitalization. We re-estimate the main models considering two clusters of banks by incorporating the classification of listed and non-listed banks in the econometric models. The estimates of these additional regressions were consistent with the results of the main analysis. The regression results for non-listed banks confirm that ESG_perf is positively related to B_size, B_ind, and CSR_com. The average age of the board of directors (B_age) remains insignificant, whereas the relationship between female directors and ESG_perf is non-linear. Nevertheless, the less significant results for listed banks can be attributed to the low number of observations in which the panel data analysis was run. The datasets of the robustness test for ESG_perf estimations are not reported in tabular form to save space and enhance the readability of this study.
5. Conclusions
This study investigated how CG variables influence bank ESG performance. We studied several features of a bank's BoD to understand which CG characteristic should be adopted to improve its ESG performance. Based on the prior research demonstrating that boards play a crucial role in oversight in financial institutions, this study explored the relationship between board diversity and ESG dimensions in the Italian banking sector for the period 2017–2021.
The empirical results showed that board size, independence, activity and the presence of a CSR/sustainability committee positively influence a bank's ESG performance, while no significant relationship was found between average board age and ESG performance. Our study also contributes to the literature on gender diversity and ESG by revealing a non-linear relationship between female directors on boards (B_gend) and ESG_perf, thereby confirming that only gender-balanced boards positively influence banks' ESG performance. Our main findings show that gender diversity positively influences a bank's ESG performance only up to a certain threshold for women on the board, which is in line with the prior literature (Schwartz-Ziv, 2017). An in-depth understanding of these relationships is a significant topic requiring further research to assess the importance of CG recommendations in the banking sector.
Given that ESG activities are becoming an important performance benchmark for stakeholders (particularly investors), the integration of ESG indicators into financial reporting seems to be the best way to increase the market share of socially responsible investments. Considering the growing attention of institutional investors and stakeholders to ESG activities, an optimal ESG performance score may lead banks to enhance their reputations, market appeal and economic performance (Cornier et al., 2011; Fayad et al., 2017; Helfaya and Moussa, 2017; Forcadell and Aracil, 2017; Brogi and Lagasio, 2018).
Our study contributes to the academic literature in several ways. First, the existing literature on this topic primarily focuses on non-financial firms, while this study concentrates solely on the banking industry. To the best of our knowledge, current studies analyzing the relationship between CG variables and sustainability in the banking sector are limited and mainly deal with ESG disclosure (and not ESG performance) (Birindelli et al., 2018; Shakil et al., 2021; Gurol and Lagasio, 2022). Second, this study advances the literature on ESG in the Italian banking sector (Perrini et al., 2006). Our findings provide the first empirical evidence of a relationship between board diversity and ESG performance in this context. Third, this study is the first to investigate the relationship between CG variables and ESG dimensions using the ESG scores provided by Refinitiv. Lastly, in the Italian banking industry, board diversity has not yet been assessed to verify how a critical mass of women on a BoD affects ESG dimensions. Hence, this study aims to fill this gap by testing whether and how ESG performance is influenced by the threshold number of female managers on BoDs.
This study has several implications for managers, investors, and policy-makers. From a managerial perspective, our study suggests that managers and CEOs should pay more attention to CG to improve ESG performance, which serves as a benchmark for sustainability-oriented investors. Because large boards achieve high levels of ESG performance, bank managers should engage both male and female directors to enlarge board size and ensure gender diversity. Our study underscores the importance of establishing a CSR/sustainability committee as a strategic tool to demonstrate banks' commitment to sustainability. Overall, the findings support regulators by providing insights into enhancing ESG performance through the design of specific governance mechanisms.
This study had several limitations. To begin with, the empirical analysis assumes that ESG performance is an effective measure of bank sustainability performance. It would be interesting to examine the impact of board diversity on ESG dimensions by adopting other ESG performance measures. Additionally, using a larger sample of financial institutions and an extensive range of time to examine how ESG performance is affected by board characteristics and composition is worth pursuing. To date, however, data availability remains an issue in these studies. Nevertheless, the limitations of this study provide opportunities for further research. First, while we studied several board characteristics (i.e. board size, average age, percentage of female directors, board activity, independent directors and presence of a CSR/sustainability committee), future research efforts could concentrate on other diversity features and critical resources held by board members (e.g. nationality, seniority, background, experience and skills) in line with the resource dependence theory. Second, we used data from only one developed country; however, this type of investigation can be extended to emerging economies to improve generalizability. Hence, future research should focus on developing countries or conduct a comparative analysis across countries to assess ESG performance.
Retraction statement
The publisher of Management Decision wishes to retract the article by Elisa Menicucci, Guido Paolucci (2023) ‘The influence of Italian board characteristics on environmental, social and governance dimensions’, published in Management Decision, Vol. 61 No. 10, pp. 3082-3105, https://doi.org/10.1108/MD-09-2022-1224. It has come to our attention that a large portion of this article is taken, without full and proper attribution, from an earlier work by Elisa Menicucci, Guido Paolucci (2022) ‘Board Diversity and ESG Performance: Evidence from the Italian Banking Sector’, Sustainability, Vol. 14 No. 20, 13447, https://doi.org/10.3390/su142013447. The submission guidelines for Management Decision make it clear that articles must be original. The authors would like it to be noted that they are not in agreement with this retraction. The publisher of the journal sincerely apologizes to the readers.
